This article delves into the significance of geriatric screening tools like G8 and VES-13 in identifying elderly cancer patients who may benefit from CGA. It highlights the potential of these assessments to predict treatment-related complications and overall survival.

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India’s population has crossed 1.428 billion by the end of April 2023 making it the world’s most populous country surpassing China. A significant proportion of these are the elderly.

Based on the National policy for older persons, Year 1999 issued by the Ministry of Social Justice and Empowerment, Government of India, and the operational guidelines of the National policy for the healthcare of the elderly (NPHCE), 2011, 60 years is taken as cut off for defining elderly population in our country.

The cancer incidence in our country as per the last GLOBOCAN report stood at a humongous 1324413 in the year 2020. By 2026, it is predicted that nearly 8.20 lakhs of this new cancer incidence will be in the geriatric population.

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Geriatric screening tools

Although a geriatric assessment is recommended to inform care, the time and expertise required limit its feasibility for all patients. Screening tools offer the potential to identify those who will benefit most from a geriatric assessment (GA).

The G8 and VES-13 have the most evidence to recommend their use to inform the need for geriatric assessment.

The G8 screening tool consists of seven items, they are:

• Dealing with food intake
• Weight loss
• Mobility
• Neuropsychological problems
• Body mass index
• Prescription drugs
• Self-perception of health

The G8 screening tool was developed specifically for elderly cancer patients. G8 takes 3–5 minutes. A total score ranges from 0 (poor score) to 17 (good score), and a score of ≤14 is considered abnormal. Sensitivity typically ranges from 65%–92% and is more than 80% in many studies detecting impairments via a full GA.

The VES-13 was developed for identifying older patients at increased risk of health deterioration in the community setting focusing on activities of daily living and instrumental activities of daily living. A score ≥ 3 indicates frailty. VES-13 may predict severe chemotherapy toxicity and survival in older patients with cancer.

Two large prospective studies:

1. CARG (Cancer and Aging Research Group).

2. CRASH (Chemotherapy Risk Assessment Scale for High-Age Patients).

Identified parameters of GA capable of predicting severe chemotherapy-related complications in a heterogeneous cancer population.

Both studies attempted to correct for differences in treatment characteristics (CRASH: MAX-2 index; CARG: poly- v mono chemotherapy and standard vs reduced dose), but these categorisations do not fully capture the diversity of specific chemotherapy drugs and schedules.

The predictive ability of these models remains moderate at the individual level, and they require further validation and optimisation.

The Cancer Aging Research Group (CARG) risk score, developed by Hurria and colleagues, is an easy-to-use tool that predicts significant chemotherapy-related toxicities (grades 3–5) in older North American adults aged ≥65 years starting on chemotherapy.

Based on their training samples and subsequent validation studies, the investigators identified low-risk, mid-risk and high-risk groups predicting increasing rates of grade 3–5 toxicities (low-risk: 30%, intermediate-risk: 52% and high-risk: 83%) with statistical significance (p<0.001). This has been validated in the Indian population also.

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Comprehensive geriatric assessment

When screening suggests potential frailties, the performance of CGA is the optimal approach to better identify risks and consequently tailor treatment, as demonstrated in numerous papers and reviewed in an SIOG Taskforce.

The main geriatric domains to be assessed in CGA are:

• Functional status (FS)
• Fatigue
• Comorbidity
• Cognition
• Mental health
• Social support
• Nutrition
• Geriatric syndromes

(e.g. Dementia, delirium, falls, incontinence, osteoporosis or spontaneous fractures, neglect or abuse, failure to thrive, constipation, polypharmacy, pressure ulcers, and sarcopenia).

However, definitive data as to whether using the same may affect outcomes is presently not available and further studies are required.

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Correlation with outcomes

There is clear evidence that GA items independently predict OS in a variety of oncology diseases and treatment settings.

Poorer OS in older patients with cancer and deficits identified in geriatric domains might potentially be explained by several factors (eg, increased risk of death resulting from causes other than cancer, increased death resulting from cancer because of less aggressive treatment, or death resulting from complications of cancer treatment).

GA has been shown to predict the risk of treatment-related complications (eg, chemotherapy toxicity or surgical risk), but toxicity prediction at the individual level remains moderate.

This is likely because individual treatment toxicity is dependent on a variety of factors, including:

• General host factors (eg, age, genetic predisposition, and capacity for metabolising drugs)
• Factors identified in a GA (eg, functional status, comorbidity, and others)
• Treatment-related aspects (eg, choice of therapy, including different regimens and drug-drug interactions)
• Tumour characteristics (ie, tumour aggressiveness affecting the host)

Although no causal relations could be determined, several general risk factors for treatment toxicity in older patients with cancer have been described.

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Unaddressed issues

Further research should investigate if there are additional or specific risk factors among patients with specific diseases receiving specific treatment types.

It also needs to focus on the cost-effectiveness of GA-directed intervention models in older patients with cancer about key outcomes such as decreasing treatment toxicity, hospitalisation, and readmissions.

Disclaimer- The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of M3 India.

About the author of this article: Dr Bipinesh Sansar, DM Medical Oncology, Associate Professor Medical Oncology at MPMMCC and HBCH, Varanasi.