Non-Biological Factors Affecting Survival in Younger Patients with Acute Myeloid Leukemia
Authors: Costa Luciano J , Borate Uma M , Mineishi Shin 
Published: October 2019

Background: Acquired chromosome abnormalities, mutations in specific genes, age, comorbidities and prior cancer therapy are some of the factors affecting survival of younger patients with acute myeloid leukemia (AML). Less is known about non-biological factors (NBFs) that may affect the outcome of AML patients.

Methods: We conducted an analysis of AML cases reported to the Surveillance Epidemiology and End Results (SEER-18) program between 2007 (year when insurance information became available) and 2011 (most recent year available). In addition to biological characteristics, we inquired the impact of NBFs: health insurance, marital status, income, level of education and distance from county of residence to the nearest blood and marrow transplant center (a surrogate for access to advanced care) on survival of adult (>18) patients younger than 65 at the time of diagnosis of AML.

Results: The analysis included 5,904 patients with a median follow up of survivors of 19 months. Median age at diagnosis was 53 (IQR 42-59), 47% were female, 64.3% non-Hispanic Whites (NHW), 10.5% non-Hispanic Blacks (NHB), 14.6% Hispanics, 9.5% Asian or Pacific Islanders (API) and 0.6% American Indian or Alaska Native (AIAN). In terms of AML subtype (as coded by ICD-O-3, comprising a mix of morphology and cytogenetically-defined entities), 51.7% were registered as having simply AML-NOS, 5.0% had t(8;21), t(16;16) or inv (16), 1.3 % had t(6;9) or t(9;11) or inv(3), 11.2% had AML-NOS M0, M1 or M2, 18.5% AML-NOS M4 or M5, 1.9% AML-NOS M6 and 0.6% AML-NOS M7 or t(1;22). Married patients were 57.7%, 24.4% were single, 10.8% divorced, 2.7% widowed. There were 18.1% of patients enrolled in Medicaid, 72.9% with other type of insurance and 6.0% uninsured. Multivariate Cox proportional hazard model confirmed AML subtype to be predictive of survival along with age and sex. NBFs independently associated with survival were insurance status, marital status and income (Figure 1 - adjusted survival curves). Patients with Insurance other than Medicaid had the best outcome while uninsured patients and patients covered by Medicaid had similarly poor survival. Married patients (and patients with unknown marital status) had better survival than patients divorced, widowed or single. Education and distance to transplant center did not predict survival (Table 1).

Conclusion: NBFs play a substantial role in determining outcomes of younger adult patients with AML and need to be considered in efforts to improve outcomes in this disease.

Table 1 – Multivariate analysis of biological and non-biological factors associated with survival in AML Factor Reference HR 95% CI P AML category <0.001 t(8;21) or t(16;16) or inv(16) AML-NOS 0.47 038-0.58 <0.001 t(6;9) or t(9;11) or inv(3) AML-NOS 1.12 0.83-1.52 0.47 NOS- M0,M1 or M2 AML-NOS 0.81 0.72-0.91 <0.001 NOS – M4 or M5 AML-NOS 0.96 0.87-1.05 0.34 NOS-M6 AML-NOS 1.02 0.80-1.31 0.85 NOS-M7, t(1;22) AML-NOS 2.04 1.41-2.95 <0.001 AML with myelodysplasia-related changes AML-NOS 1.07 0.93-1.23 0.31 Therapy-related AML AML-NOS 1.09 0.90-1.33 0.38 Age Per 1 year 1.02 1.02-1.03 <0.001 Female Male 0.89 0.83-0.95 0.001 Marital status <0.001 Single Married 1.23 1.12-1.35 <0.001 Divorced Married 1.15 1.03-1.29 0.01 Widowed Married 1.16 0.94-1.42 0.18 Unknown Married 0.99 0.82-1.19 0.89 Insurance status <0.001 Medicaid Insured 1.21 1.11-1.34 <0.001 Uninsured Insured 1.23 1.06-1.43 0.006 Unknown Insured 1.46 1.19-1.80 <0.001 Household income > median 0.91 0.85-0.98 0.008 Race-ethnicity 0.009 AIAN NHW 0.80 0.48-1.34 0.40 API NHW 1.01 0.89-1.14 0.86 Hispanic NHW 1.08 0.97-1.20 0.15 NHB NHW 1.22 1.10-1.37 <0.001 Unknown NHW 0.73 0.39-1.37 0.33

Figure 1 Figure 1.

Disclosures

No relevant conflicts of interest to declare.