RESUMEN
BACKGROUND: Certain populations have been historically underrepresented in clinical trials. Broadening eligibility criteria is one approach to inclusive clinical research and achieving enrollment goals. How broadened trial eligibility criteria affect the diversity of eligible participants is unknown. METHODS: Using a nationwide electronic health record-derived deidentified database, we identified a retrospective cohort of patients diagnosed with 22 cancer types between April 1, 2013 and December 31, 2022 who received systemic therapy (N=235,234) for cancer. We evaluated strict versus broadened eligibility criteria using performance status and liver, kidney, and hematologic function around first line of therapy. We performed logistic regression to estimate odds ratios for exclusion by strict criteria and their association with measures of patient diversity, including sex, age, race or ethnicity, and area-level socioeconomic status (SES); estimated the impact of broadening criteria on the number and distribution of eligible patients; and performed Cox regression to estimate hazard ratios for real-world overall survival (rwOS) comparing patients meeting strict versus broadened criteria. RESULTS: When applying common strict cutoffs for eligibility criteria to patients with complete data and weighting each cancer type equally, 48% of patients were eligible for clinical trials. Female (odds ratio, 1.30; 95% confidence interval [CI], 1.25 to 1.35), older (age 75+ vs. 18 to 49 years old: odds ratio, 3.04; 95% CI, 2.85 to 3.24), Latinx (odds ratio, 1.46; 95% CI, 1.39 to 1.54), non-Latinx Black (odds ratio, 1.11; 95% CI, 1.06 to 1.16), and lower-SES patients were more likely to be excluded using strict eligibility criteria. Broadening criteria increased the number of eligible patients by 78%, with the strongest impact for older, female, non-Latinx Black, and lower-SES patients. Patients who met only broadened criteria had worse rwOS versus those with strict criteria (hazard ratio, 1.31; 95% CI, 1.27 to 1.34). CONCLUSIONS: Data-driven evaluation of clinical trial eligibility criteria may optimize the eligibility of certain historically underrepresented groups and promote access to more inclusive trials. (Sponsored by Flatiron Health.).