Trajectories of Frailty Categorization over Time Among Real-World Patients with Multiple Myeloma: A Prospective Cohort Study (MFRAIL)
Bibliographic record
Abstract
Introduction Multiple myeloma (MM) is plasma cell neoplasm of older adults with frail individuals being at an increased risk of poor outcomes including worse survival as well as increased toxicity. Several tools have been developed to assess frailty to categorize patients from fit to frail. However, current tools have been designed to assess frailty at a single timepoint at diagnosis. Given frailty is dynamic in nature, the objective of this analysis was to further understand how frailty categorization may change over time using three commonly utilized MM frailty assessment tools among real-world patients. Methods MFRAIL is an ongoing prospective cohort study conducted in three medical centers in Ontario, Canada. Participant recruitment began in August 2021, targeting patients initiating treatment for newly-diagnosed or relapsed MM. Eligibility criteria required participants to be age > 18, and start treatment within six weeks of study enrolment. Demographic, MM specific, and functional characteristics were assessed at baseline. Frailty was evaluated at baseline and at a 12-month follow-up using the following three frailty assessment tools: 1) the IMWG frailty index (Palumbo et al. 2015), 2) the Simplified Frailty Score (Facon et al. 2020), and 3) the Mayo Frailty Score (Milani et al. 2016). Both the absolute frailty scores (ranging from 0-5) as well as the frailty categorization were calculated. Results 100 patients enrolled, 99 completed baseline assessments and 82 patients completed 12-month follow-up assessments (9 deceased, 2 transitioned to long term care, and 6 withdrew). The baseline characteristics have previously been reported (Haider et al. 2024) including the variable categorization of patients classified as frail. The 12 month follow up data is highlighted below. At the 12 month follow-up period, the IMWG frailty index classified 37 (45%) patients as fit, 21 (26%) as intermediate fit, and 24 (29%) as frail. Of the 41 fit patients at baseline, 33 (80%) had no change in the absolute frailty score and remained fit, while 2 (5%) had a deterioration with 1 patient becoming intermediate fit and the other becoming frail. Amongst the 34 intermediate fit patients at baseline, 17 (50%) had no change in the absolute frailty score and remained intermediate fit, 2 (6%) had an improvement and became fit, while 5 (15%) had a deterioration and became frail. Of the 41 frail patients at baseline, 11 (27%) had no change in absolute frailty score, 11 (27%) had an improvement, and 1 (2%) had a deterioration. This corresponded to 18 (44%) frail patients remaining frail, 2 (5%) becoming fit and 3 (7%) becoming intermediate fit. Spearman's ρ between baseline and 12-month follow-up scores was 0.82. At 12 months, the simplified frailty score classified 42 (51%) patients as non-frail and 40 (49%) as frail. Of the 50 non-frail patients at baseline, 33 (66%) had no change in absolute frailty score remaining non-frail, while 9 (18%) had a deterioration becoming frail. Amongst the 66 frail patients at baseline, 17 (26%) had no change in absolute frailty score, 18 (27%) had an improvement, and 5 (8%) had a deterioration. This corresponded to 31 (47%) frail patients remaining frail, while 9 (14%) became non-frail. Spearman's ρ between baseline and 12-month follow-up scores was 0.74. The Mayo frailty score categorized 15 (18%) patients as Stage I, 35 (43%) patients as Stage II, 24 (29%) as Stage III, 3 (4%) as Stage IV and 5 (6%) as unknown frailty status. Of the 19 Stage I patients at baseline, 11 (58%) remained Stage I, 4 (21%) became Stage II, and 2 (11%) became Stage III. Amongst the 39 Stage II patients at baseline, 18 (46%) remained Stage II, 3 (8%) became Stage I, and 6 (15%) became Stage III. Of the 22 Stage III patients at baseline, 7 (32%) remained Stage III, and 5 (23%) became Stage II. Out of the 18 Stage IV patients, 3 (17%) remained Stage IV, 1 (6%) became Stage II, and 6 (33%) became Stage III. Spearman's ρ between baseline and 12-month follow-up scores was 0.67. Conclusion The distribution of frail vs non-frail patients demonstrated a substantial change over time. This highlights that a one-time, baseline frailty measurement may not be adequate for stratification or prediction of outcomes both in the real-world as well as in clinical trials. Lastly, changes in continuous absolute frailty score may be better suited for dynamic measurements and capture early improvement/deterioration prior to changes observed in frailty classification.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".