NcXHF

KLEPIN, RODIN, AND HURRIA
patient–specifıc palliative chemotherapy trials, functional
outcomes, health care utilization, cognitive function, and
quality of life were reported in 6%, 3%, 6%, and 31%, respectively.
76 Yet many of these outcomes are of concern to
patients. Use of GA measures during treatment can provide
useful information to inform risk prediction and supportive
care. For example, an observational study of 364 adults age 70 or
older (77% with ECOG 0 to 1) starting fırst-line chemotherapy
showed that almost 17% experienced a loss of independence defıned
by a decrease in the ability to complete ADLs after the fırst
treatment. 77 Incorporation of simple assessment measures during
treatment in clinical trials and in practice may better inform
management of complications to ultimately improve treatment
effıcacy and tolerance.
CONCLUSION
Caring for older patients with cancer presents unique challenges.
Incorporation of GA strategies can help individualize
initial treatment decisions and inform management strategies
during the course of treatment and survivorship. Incorporation
of GA strategies into clinical trial design will
provide needed evidence to optimize therapy for vulnerable
and frail older adults.
Disclosures of Potential Conflicts of Interest
Relationships are considered self-held and compensated unless otherwise noted. Relationships marked “L” indicate leadership positions. Relationships marked “I” are those held by an immediate
family member; those marked “B” are held by the author and an immediate family member. Institutional relationships are marked “Inst.” Relationships marked “U” are uncompensated.
Employment: None. Leadership Position: None. Stock or Other Ownership Interests: None. Honoraria: None. Consulting or Advisory Role: Arti Hurria,
GTx, Seattle Genetics, Boehringer Ingelheim Pharmaceuticals, Inc. Speakers’ Bureau: None. Research Funding: Arti Hurria, GSK (Inst), Celgene (Inst).
Patents, Royalties, or Other Intellectual Property: None. Expert Testimony: None. Travel, Accommodations, Expenses: None. Other Relationships:
None.
References
1. U.S. Department of Commerce. Population projections of the United
States by age, sex, race, and Hispanic origin: 1995 to 2050. http://www.
census.gov/prod/1/pop/p25-1130/p251130.pdf. Accessed March 4,
2015.
2. Smith RA, Manassaram-Baptiste D, Brooks D, et al. Cancer screening in
the United States, 2014: a review of current American Cancer Society
guidelines and current issues in cancer screening. CA Cancer J Clin.
2014;64:30-51.
3. Institute of Medicine. Retooling for an Aging America: Building the
Health Care Workforce. Washington, DC: The National Academies
Press; 2008.
4. Kirkwood MK, Kosty MP, Bajorin DF, et al. Tracking the workforce: the
American Society of Clinical Oncology workforce information system.
J Oncol Pract. 2013;9:3-8.
5. Reuben DB, Rubenstein LV, Hirsch SH, et al. Value of functional status
as a predictor of mortality: results of a prospective study. Am J Med.
1992;93:663-669.
6. Inouye SK, Peduzzi PN, Robison JT, et al. Importance of functional
measures in predicting mortality among older hospitalized patients.
JAMA. 1998;279:1187-1193.
7. Lee SJ, Lindquist K, Segal MR, et al. Development and validation of a
prognostic index for 4-year mortality in older adults. JAMA. 2006;295:
801-808.
8. Walter LC, Brand RJ, Counsell SR, et al. Development and validation of
a prognostic index for 1-year mortality in older adults after hospitalization.
JAMA. 2001;285:2987-2994.
9. Seeman TE, Kaplan GA, Knudsen L, et al. Social network ties and mortality
among the elderly in the Alameda County Study. Am J Epidemiol.
1987;126:714-723.
10. Landi F, Onder G, Gambassi G, et al. Body mass index and mortality
among hospitalized patients. Arch Intern Med. 2000;160:2641-2644.
11. Seeman TE, Berkman LF, Kohout F, et al. Intercommunity variations in
the association between social ties and mortality in the elderly. A comparative
analysis of three communities. Ann Epidemiol. 1993;3:325-335.
12. Kanesvaran R, Wang W, Yang Y, et al. Characteristics and treatment
options of elderly Chinese patients with cancer as determined by Comprehensive
Geriatric Assessment (CGA). J Geriatr Oncol. 2014;5:171-
178.
13. Cappellani A, Di Vita M, Zanghi A, et al. Prognostic factors in elderly
patients with breast cancer. BMC Surgery. 2013;13:S2.
14. Ellis G, Langhorne P. Comprehensive geriatric assessment for older
hospital patients. Br Med Bull. 2004;71:45-59.
15. Yourman LC, Lee SJ, Schonberg MA, et al. Prognostic indices for older
adults: a systematic review. JAMA. 2012;307:182-192.
16. Aparicio T, Girard L, Bouarioua N, et al. A mini geriatric assessment
helps treatment decision in elderly patients with digestive cancer. A pilot
study. Crit Rev Oncol Hematol. 2011;77:63-69.
17. Extermann M, Boler I, Reich RR, et al. Predicting the risk of chemotherapy
toxicity in older patients: the Chemotherapy Risk Assessment
Scale for High-Age Patients (CRASH) score. Cancer. 2012;118:
3377-3386.
18. Freyer G, Geay JF, Touzet S, et al. Comprehensive geriatric assessment
predicts tolerance to chemotherapy and survival in elderly patients with
advanced ovarian carcinoma: a GINECO study. Ann Oncol. 2005;16:
1795-1800.
19. Hamaker ME, Seynaeve C, Wymenga AN, et al. Baseline comprehensive
geriatric assessment is associated with toxicity and survival in elderly
metastatic breast cancer patients receiving single-agent chemotherapy:
results from the OMEGA study of the Dutch breast cancer trialists’
group. Breast. 2014;23:81-87.
20. Hurria A, Togawa K, Mohile SG, et al. Predicting chemotherapy toxicity
in older adults with cancer: a prospective multicenter study. J Clin Oncol.
2011;29:3457-3465.
21. Mor V, Wilcox V, Rakowski W, et al. Functional transitions among the
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