Adaptation and Performance of the Self-Report-Generated Charlson Comorbidity Index in the Lymphoma Epidemiology of Outcomes (LEO) Cohort.
Academic Article
Overview
abstract
Newly diagnosed patients with non-Hodgkin lymphoma (NHL) often have a history of other diseases, and these comorbidities can impact patient treatment and management options, as well as overall survival (OS). We developed the Lymphoma Epidemiology of Outcomes (LEO) comorbidity index (LCI) as a sum of 10 comorbidities adapted from the Self-Report-Generated Charlson Comorbidity Index (SRG-CCI) for use in the LEO cohort, a national prospective study of newly diagnosed NHL. Of the 5502 participants with self-reported comorbidity data, 3107 (56.4%) were male and the mean age at diagnosis was 60.9 years (range, 18-99 years). The LCI ranged from 0 to 6, with 48.6% having 0, 30.2% having 1, 21.2% having 2 or more comorbidities. With a median follow-up of 62.4 months among surviving participants, 2099 patients had an event and 1219 died. Continuous LCI similarly predicted both 1-year mortality (c-statistic = 0.654) and OS (c-statistic = 0.655), while it showed a weaker but still statistically significant predictive ability for lymphoma-specific (c-statistics = 0.617) and event-free (c-statistic = 0.574) survival. Participants with 1 (HR = 1.21, 95% CI 1.05-1.39) and 2+ (HR = 1.80, 95% CI 1.56-2.08) comorbidities had inferior OS compared to those with no comorbidities after adjustment for age and sex (c-statistic = 0.654), and performance strengthened after adjustment for the International Prognostic Index (c-statistic = 0.672). LCI predicted OS most strongly in marginal zone (c-statistics = 0.748) and weakest in T-cell (c-statistic = 0.579) lymphoma. The cumulative incidence of death due to lymphoma, lymphoma treatment, and other causes all increased with increasing comorbidities, with the greatest increase observed for death due to other causes. The LCI performs comparable to other published comorbidity indices, supporting its use in the LEO cohort to better model real-world outcomes and more generally providing an approach to implementing comorbidity indices in cancer survivorship cohorts.
