Performance of Four Frailty Classifications in Older Patients With Cancer: Prospective Elderly Cancer Patients Cohort Study

Abstract

Purpose Frailty classifications of older patients with cancer have been developed to assist physicians in selecting cancer treatments and geriatric interventions. They have not been compared, and their performance in predicting outcomes has not been assessed. Our objectives were to assess agreement among four classifications and to compare their predictive performance in a large cohort of in- and outpatients with various cancers. Patients and Methods We prospectively included 1,021 patients age 70 years or older who had solid or hematologic malignancies and underwent a geriatric assessment in one of two French teaching hospitals between 2007 and 2012. Among them, 763 were assessed using four classifications: Balducci, International Society of Geriatric Oncology (SIOG) 1, SIOG2, and a latent class typology. Agreement was assessed using the κ statistic. Outcomes were 1-year mortality and 6-month unscheduled admissions. Results All four classifications had good discrimination for 1-year mortality (C-index ≥ 0.70); discrimination was best with SIOG1. For 6-month unscheduled admissions, discrimination was good with all four classifications (C-index ≥ 0.70). For classification into three (fit, vulnerable, or frail) or two categories (fit v vulnerable or frail and fit or vulnerable v frail), agreement among the four classifications ranged from very poor (κ ≤ 0.20) to good (0.60 < κ ≤ 0.80). Agreement was best between SIOG1 and the latent class typology and between SIOG1 and Balducci. Conclusion These four frailty classifications have good prognostic performance among older in- and outpatients with various cancers. They may prove useful in decision making about cancer treatments and geriatric interventions and/or in stratifying older patients with cancer in clinical trials.

Frailty and long-term mortality of older breast cancer patients: CALGB 369901 (Alliance)

“…44 Other indices such as e-prognosis include some aspects of deficits accumulation, but were developed for general populations 17 but an under-estimate short-term survival of breast cancer patients 20 who have better non-cancer survival than age-matched peers without cancer. 45-47 Other frailty tools that combine deficits accumulation and phenotypic measures have been developed in younger breast cancer populations, 48 and for patients with multiple types of cancer, 7,49,50 with varying results across cancer types, 50,51 suggesting that disease-specific tools may be needed.…”

“…These two approaches to frailty measurement should be compared for accuracy and effort in data collection. 50 Next, the frailty index relied on self-report for the period prior to diagnosis. Since self-report is fairly accurate 56 or over-estimates function, self-report is likely to bias results for the association between the deficits accumulation frailty index and death toward the null.…”

Merkel cell carcinoma

“…Patients >75 years of age are less tolerant to multi-modal treatment 140 , which could involve combinations of loco-regional surgery, loco-regional radiotherapy and systemic cytotoxic, targeted or immune therapy. Consequently, seeking an onco-geriatric assessment in selected patients before deciding a management course should be considered.…”

Using Information Technology in the Assessment and Monitoring of Geriatric Oncology Patients

“…On the other hand, CGA domains such as functional and cognitive impairment, weight loss, lack of social support and increased comorbidity burden are associated with treatment toxicity [2, 10, 21–23]. Depression, polypharmacy, and burden of symptoms, in addition to the aforementioned domains, are also associated with poorer overall survival [24–28]. For older patients with cancer undergoing surgery, CGA domains also predict a higher risk of post-operative complications, hospitalizations and mortality [29–31].…”