Disparity in Utilization of Multiagent Therapy for Acute Promyelocytic Leukemia in the United States

Despite high rate of cure in acute promyelocytic leukemia (APL) in clinical trials, outcomes in real-world practice are dismal. We utilized National Cancer Database (NCDB) to explore utilization of multiagent therapy in APL and identify any disparities in treatment in real-world practices.

NCDB categorizes use of systemic chemotherapy into single agent versus multiagent therapy. Some patients received hormonal therapy, immunotherapy, and unknown therapy; details of these treatments could not be ascertained. We therefore used multiple logistic regression analysis to evaluate effects of covariates on the probability of multiagent therapy use in 6678 patients.

Compared to patients >60 years, patients aged 0 to 18 years (hazard ratio[HR] 3.2, 95% confidence interval [CI] 1.8-5.5, P< .0001), 19 to 40 years (HR 1.6, 95% CI 1.03-2.54, P= .03), and 41 to 60 years (HR 1.6, 95% CI 1.3-1.9, P< .0001) were more likely to receive multiagent therapy. Patients with Charlson comorbidity index (CCI) of 0 (HR 1.6, 95% CI 1.2-2.3, P= .001) and CCI of 1 (HR 1.4, 95% CI 1.0-1.9, P= .04) had a higher likelihood of receiving multiagent therapy than patients with CCI ≥ 3. Patients treated at academic cancer centers, compared to those treated at community cancer center (HR 0.5, 95% CI 0.3-0.7, P= .001), comprehensive community cancer center (HR 0.7, 95% CI 0.6-0.8, P< .0001), and integrated network cancer center (HR 0.8, 95% CI 0.6-0.9, P= .02) were more likely to be treated with multiagent therapy. Compared to the patients with private insurance, those with Medicaid had increased likelihood (HR 1.2, 95% CI 1.0-1.4, P= .04) whereas uninsured patients had a lower likelihood of receiving multiagent therapy (HR 0.6, 95% CI 0.5-0.8, P= .0005).

To our knowledge, this study is the first and the largest scale analysis of treatment practices in APL in real-world practices. Our findings highlight significant disparities in treatment of APL based on age, insurance, and health-system factors.