Comorbidity burden and outcomes of older adults with acute promyelocytic leukemia: a National Cancer Database analysis of 2221 patients.

Association between comorbidity burden and patient outcomes has not been adequately investigated in acute promyelocytic leukemia (APL). We utilized the National Cancer Database to evaluate the association of the Charlson-Deyo Comorbidity Index (CCI) with one-month mortality and overall survival (OS) in adults ≥60 years with APL. One-month mortality was 16%, 24%, and 32%, and 3-year OS was 61%, 53%, and 38% for patients with CCI 0, 1, and ≥2, respectively. One-month mortality was higher for patients with CCI 1 (OR 1.67, 95% CI 1.29-2.16, p < .001) and CCI ≥ 2 (OR 2.31, 95% CI 1.70-3.13, p < .001) compared to patients with CCI 0. Patients with CCI 1 (HR 1.27, 95% CI 1.10-1.46, p < .001) and CCI ≥ 2 (HR 1.74, 95% CI 1.48-2.06, p < .001) had worse OS compared to patients with CCI 0. In conclusion, CCI is an independent predictor of survival outcomes in patients with APL.

Association of insurance types and outcomes in acute promyelocytic leukemia.

Understanding the association between insurance status and survival in an evolving US healthcare system remains a challenge but is essential to address healthcare disparities. We utilized National Cancer Database to evaluate the effects of insurance type on one-month mortality and overall survival (OS) in patients with acute promyelocytic leukemia. Among patients <65 years, one-month mortality was worse for uninsured patients and patients with Medicare compared to patients with private insurance. OS was similar between patients with private insurance and uninsured patients but worse for patients with Medicare and Medicaid/other government insurance. In multivariate analysis, older age and greater comorbidity burden conferred worse OS. For patients ≥65 years, insurance type did not affect one-month mortality and OS. Older age, greater comorbidity burden, and treatment at non-academic centers conferred worse one-month mortality and OS. Our results highlight healthcare disparities based on insurance types for both younger and older patients.

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

BACKGROUND: 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. PATIENTS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.