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3306 Feasibility of Routine Frailty Screening Assessment for Patients in a Hematologic Oncology Clinic: Results from a Pilot Study

Health Services and Outcomes Research – Malignant Diseases
Program: Oral and Poster Abstracts
Session: 902. Health Services and Outcomes Research – Malignant Diseases: Poster II
Sunday, December 6, 2015, 6:00 PM-8:00 PM
Hall A, Level 2 (Orange County Convention Center)

Gregory A. Abel, MD, MPH1, Tammy T. Hshieh, MD2*, Nolan B. Condron1*, Caron A. Jacobson, MD1, David P. Steensma, MD1, Jacob P. Laubach, MD1, Richard M. Stone, MD3 and Jane A. Driver, MD, MPH2*

1Dana-Farber Cancer Institute, Boston, MA
2Division of Aging, Brigham and Women's Hospital, Boston, MA
3Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA

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:

Nolan_2.JPG

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.

 

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 .

*signifies non-member of ASH