Background and Rationale

The treatment of geriatric cancer patients has to be cognizant of their vulnerabilities. Oncologic interventions require to be tailor made to the chronological as well as biological age of patients as well as to their social situation. Geriatric assessment helps to identify areas of special concern as well as to design interventions to address them.

There is limited information regarding vulnerabilities among the elderly cancer patient in India especially with reference to patients undergoing radiotherapy and surgery. This study aims to fill this gap.

General aim

Pilot study to record comprehensive evaluation of elderly patients before treatment decisions and to prospectively evaluate the association of G8 assessment with assessment-based intervention, clinical outcomes and treatment-related severe toxicity

Primary objective (endpoint)

·       Identification of vulnerable geriatric cancer patients using G8.

Secondary objectives (endpoints)           

·       Demographic details, comorbidities and status as per G8 screening tool.

·       Prospective record of geriatric assessment-based interventions in patients identified as vulnerable.

·       Prospective record of treatment modifications at initiation due to elderly / G8 screening status of patient (0,3 months).

·       Treatment breaks/ reduced intensity/omission

·       Prolongation of treatment time

·       Hospital admission

·       ICU admission

·       Falls

·       Deterioration in G8 score

·       Toxicity (Acute and late toxicity according to CTCAE version 5.0 criteria at baseline and 3 months)

·       Quality of life EORTC QLQ-C30, QLQ-ELD14 at 0 and 3 months

Design 

Prospective registry-based study

Population

All patients above 65 years registered at APCC for cancer diagnosis/treatment

Statistical considerations

Descriptive statistical analysis will be applied to routine demographic data collected, expressed as frequency for categorical variables. Chi-square test and Fisher’s exact test will be used for comparisons. Kaplan Meir curves will be used to summarize the time to event endpoints over time. Log-rank test will be used to test for differences in the OS for dichotomous variables. Factors significant in the univariate analysis will be tested by multivariate analysis using the Cox proportional hazard model.