Feasibility of Routine Frailty Screening Assessment for Patients in a Hematologic Oncology Clinic: Results from a Pilot Study
Bibliographic record
Abstract
Abstract Background: Although frailty is felt to influence outcomes in hematologic oncology, formal frailty assessment is rarely performed in this patient population. We report preliminary data from a pilot study initiated with the goal of identifying patients who would benefit from comprehensive geriatric assessment (CGA) by an embedded geriatrician at the Dana-Farber Cancer Institute (DFCI). Methods: Starting in February of 2015,all patients aged 75 and older who present for an initial consultation for hematologic malignancy at DFCI have been approached for evaluation by a trained clinic assistant. In a 15-minute interview that occurs before they see the oncologist, the assistant uses two parallel methods of screening to characterize the patient as frail, pre-frail, or robust. The first employs a "cumulative deficit" approach (Rockwood, 2007) including 26 questions adapted from the Yale Precipitating Events Project (Searle, 2008), as well as the delayed recall section of the Montreal Cognitive Assessment (Nasreddine, 2005), a clock-in-the-box test (Chester, 2011), a grip strength test (Gill, 2006), and a gait speed test (Studenski, 2011). The second is a "phenotype" approach (Fried, 2001), which gives equal weight to performance on the gait speed and grip strength tests, as well as three questions about weight loss, energy expenditure and self-reported exhaustion. Patients are assigned frailty designations with each method, and a combined designation (the worse of the two if they do not match). Frail and pre-frail patients on the combined assignment are randomized to see an embedded geriatrician for CGA at a subsequent visit. For patients with cancers that typically require intervention (eg, we excluded cancers such as low-risk MDS and indolent CLL), we assessed whether frailty category correlated with subsequent treatment choice (chemotherapy vs supportive care) within two months of assessment. Results: As of August 1, 2015, 105 patients aged 75 or older have been approached; 86 (82%) have agreed to be assessed. The most common reasons for refusal were (categories not mutually exclusive): anxiety about potential results (50%), patient not planning to return to DFCI (39%), and patient feeling s/he was not in need of geriatric services (22%). The median age of those assessed was 78 years; 41% were female, and 33% were seen at the leukemia clinic, 31% at the lymphoma clinic, and 36% at the myeloma clinic. Overall, on the combined measure, 15% of patients were frail, 54% pre-frail, and 31% robust. Agreement between the cumulative deficit and phenotype methods was moderate (weighted kappa = .65). For those patients with cancers that typically require intervention (n=57), treatment status at two months by the different assessments systems is shown below: Conclusion: Routine screening frailty assessment of older blood cancer patients by a non-MD clinic assistant is feasible and informative. So far, well over half of the patients in this pilot have been found to be frail or pre-frail, and would likely benefit from CGA and follow-up. Regardless of frailty designation, most patients with cancers that typically require intervention were treated with chemotherapy rather than supportive care. Moreover, for those patients, the cumulative deficit technique was better at predicting subsequent treatment status. Figure 1. Figure 1. Disclosures Steensma: Incyte: Consultancy; Amgen: Consultancy; Celgene: Consultancy; Onconova: Consultancy. Laubach:Novartis: Research Funding; Onyx: Research Funding; Celgene: Research Funding; Millennium: Research Funding. Stone:Celator: Consultancy; Merck: Consultancy; Amgen: Consultancy; Abbvie: Consultancy; Roche/Genetech: Consultancy; AROG: Consultancy; Novartis: Research Funding; Sunesis: Consultancy, Other: DSMB for clinical trial; Agios: Consultancy; Juno: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; Pfizer: 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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".