Using the Geriatric Core Dataset (G-CODE) to predict mortality in older patients with cancer: development and external validation.

Abstract

Background: The Geriatric Core Dataset (G-CODE) was developed to standardise geriatric assessment (GA) in older adults with cancer. This study aimed to develop and externally validate a mortality prediction score based on the G-CODE at 6-month (main endpoint), 12- and 24-month (secondary endpoints), in older patients with cancer.

Methods: Data came from two prospective French cohorts of cancer patients aged ≥70 referred for GA: the Elderly Cancer Patients (ELCAPA) cohort (training set) and the Geriatric Oncology Marseille (GOM) cohort (validation set). G-CODE items were evaluated using multivariate Cox regression analyses, with Akaike information criterion selection. Discrimination was assessed using time-dependent area under the receiver operator curve (tAUC) for 6-, 12-, and 24-month mortality.

Results: We included 950 and 766 patients in the training and validation sets (mean ± sd age: 83 ± 6 years/81 ± 6 years; men: 55%/59%; 6-month mortality: 27%/22%). The final G-CODE model highlighted five strong predictors of 6-month mortality: not having a caregiver, weight loss, comorbidities, an impaired 4-instrumental activities of daily living score, and an impaired timed-up-and-go test. The tAUCs of the model, including the G-CODE, age, cancer type, metastases, and treatment, were 82%, 78% and 80%.

Conclusions: The final G-CODE score (range: 0-17) accurately predicts 6-, 12-, and 24-month mortality.

Clinical trial registration: NCT02884375 (ClinicalTrials.gov).