Handgrip Strength - Finetuning an Objective Measure of Frailty in Transplant-Eligible Patients with Multiple Myeloma
Bibliographic record
Abstract
Background Frailty assessment has emerged as a useful tool to predict treatment toxicity and efficacy in older, transplant-ineligible patients with multiple myeloma (MM). Frailty testing might also be useful in younger patients to identify those less fit who are destined to suffer undue toxicity from transplant. However, we previously reported that current frailty tools and definitions have limited ability to discriminate amongst transplant-eligible patients, with most clustering into fit or prefrail categories (Devasia ASH 2022). Therefore, frailty-defining thresholds must be redefined and validated for this younger, fitter population. Handgrip strength is a simple objective tool that has been used to predict morbidity and mortality in various populations and in our experience, has the highest completion rate amongst the objective functional tools. Thus, we aimed to further evaluate handgrip strength and the optimal thresholds that would best identify less fit patients preparing for transplant. Method In an ongoing prospective trial at our centre, MM patients undergo a battery of objective and subjective frailty assessments prior to autologous stem cell transplant. Various handgrip strength thresholds were derived firstly by obtaining the mean and standard deviation (SD) of our cohort, a healthy US (Wang J Ortho Sports 2018), and Canadian population (Wong Stats Can 2016), stratified by gender, age, weight and height. We then constructed SD thresholds (0.5 SD, 1SD, 1SD-5kg, 1.5SD, 2SD) below the mean. These were then correlated with frailty parameters using: subjective frailty tools (Karnofsky, ECOG, Rockwood, Lawton ADL, Edmonton symptoms [ESAS], exhaustion and weight loss scores), tests of organ function (LVEF, CrCl, PFT, cell counts of marrow function), comorbidity indices (Charlson, haematopoietic cell transplant specific [HCT-CI]), and integrated myeloma-specific frailty scores (IMWG-geriatric assessment tool [IMWG-GAT], Revised Myeloma Comorbidity Index [R-MCI]). T-test was used for continuous variables; chi2 or Fisher's exact test as appropriate for categorical variables. All P values were 2 sided and statistically significant at P <0.05. Statistical analysis was performed using SAS system version 9.4. Results To date, 352 study patients have undergone frailty testing prior to transplant. Of these, 23 (7%) had incomplete data, thus 329 (93%) were included in our analysis. Overall, we found that the handgrip threshold of 1.5 SD below the mean of a healthy Canadian population stratified by gender and age, identified 76/329 (23%) of our patients and correlated with the largest number of frailty parameters (referred hereon as “weak” handgrip). When compared to all others, those patients with “weak” handgrip had lower organ function measures, including mean hemoglobin (p=0.03), albumin (p<0.01), LVEF (p=0.03), FEV1 (p=0.04), FVC (p<0.01) as well as worse subjective symptom/function scores, including higher ESAS (p<0.01), ECOG (p<0.01) , Karnofsky (p<0.01), Rockwood (p<0.01), Lawton ADL (p<0.01). Patients with “weak” handgrip also had more comorbidities as per Charlson (p<0.01) and HCT-CI (p=0.01), and rated less fit using the myeloma-specific IMWG-GAT (p<0.01). From a feasibility perspective, we reported handgrip testing had 100% completion rate, whilst up to 9.1% were unable to complete walk-based tests (6 minute walk, timed-up-and-go), mostly due to limitations from bone pain. Finally, we tested various models combining handgrip strength with “subjective” measures of patient function (ESAS, ADL, Rockwood, Karnofsky). This allowed us to further fine-tune the most unfit proportion of our cohort to 10-15%. Conclusion Handgrip strength is a quick and simple test which shows promise as a consistently feasible frailty assessment tool for transplant-eligible MM patients. Thresholds to define weak grip strength must be appropriate for each population. In this analysis, thresholds of 1.5 SD below the mean of healthy Canadian population, stratified by gender and age performed the best and correlated well with other frailty assessments. We next plan to employ this new threshold for handgrip strength in combination with subjective functional measures in our ongoing study of transplant-eligible patients and correlate with post-transplant toxicity. This may facilitate personalization of supportive care (e.g. selected antibiotic prophylaxis) and risk counseling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".