Comparative Effectiveness of Chemotherapy in an Elderly Acute Myeloid Leukemia Population in the United States

Abstract

Introduction: A disproportionate number of newly diagnosed acute myeloid leukemia (AML) occurs in elderly patients. Conventional chemotherapy treatments for AML may be highly toxic and elderly patients often have increased comorbidity burden and loss of organ reserve that may impact their ability to tolerate therapy. Consequently, fewer than half of elderly patients with AML receive anti-leukemic therapy and outcomes among these patients have not improved in the last few decades. We described treatment patterns and outcomes of elderly AML patients in a real-world population.Methods: We utilized a retrospective cohort analysis of first primary AML patients in the linked Surveillance, Epidemiology, and End Results (SEER)-Medicare database. Patients were diagnosed between January 1, 2000 to December 31, 2009, >66 years, and continuously enrolled in Medicare Part A and B in the year prior to diagnosis. There were 8336 patients identified, of which 5009 (60%) did not receive treatment (NT). Chemotherapy agent definition was not possible in approximately 70% of patients who received therapy because chemotherapy was administered during inpatient stays which are paid based on ICD-9 diagnosis or procedures codes only and not chemotherapy codes. Of those initiating treatment within 3 months after diagnosis, 345 were treated with azacitidine + decitabine (AD) and 124 were treated with cytarabine+anthracycline (CA). Statistical comparisons were made between NT, AD and CA. To compare demographic and clinical characteristics by treatment, the Chi-square test for categorical variables and ANOVA or t-test for continuous variables was used. Kaplan-Meier curves and corresponding log-rank test were used to compare overall survival by treatment. Cox regression and propensity-weighted analyses estimated the relative risk of death adjusting for demographic and clinical factors. The date of last follow-up was December 31, 2010.Results: Patients receiving CA were younger (mean age 73 vs. 78 and 81), more likely male (62% vs. 59% and 50%), married (71% vs. 61% and 47%), had less secondary AML (7% vs. 21% and 19% with prior myelodysplastic syndrome [MDS]), less likely to have poor performance indicators (2% vs. 9% and 17%) and had lower comorbidity score compared to those receiving AD and NT, respectively. Similarities in age, comorbidity burden and proportion with high risk disease were noted in AD and NT patients. The median unadjusted overall survival was longer for patients treated with CA (18.9 months) compared to AD (6.6 months) and NT (1.7 months; log rank p <.0001). After adjusting for all covariates in the survival model, a 69% reduction in mortality was observed among patients treated with CA and a 53% reduction in mortality was observed among patients treated with AD, compared to those who did not receive any treatment (Table 1). The survival benefit of AD and CA were maintained in the younger (≤75) and older (>75) cohorts. Increasing age, increasing comorbidity score and presence of poor performance indicators were associated with significant increases in mortality. Propensity score adjusted multivariate analysis demonstrated similar risk reductions for CA (HR=0.41; 95% CI=0.34-0.49) and AD (HR=0.49; 95% CI=0.44-0.55).Conclusions: A substantial proportion of elderly AML patients are not receiving therapy for their disease. We observed a significant survival benefit with receiving treatment, even among the AD group who had similar characteristics to the NT group. Further, improved survival after receiving CA compared to AD was noted, but the propensity score adjusted analysis demonstrated similar reductions in death. The findings from this study can provide an important baseline for evaluating the benefits of new treatments under investigation.Table 1Multivariate Overall Survival AnalysisCovariates*nHR95% CITreatmentNot Treated5009refAD3450.470.42-0.53CA1240.310.25-0.39Age66-70537ref71-759201.311.17-1.4676-8012761.421.27-1.58>8027451.681.52-1.86Poor Performance IndicatorsNo4605refYes8731.301.20-1.41*Model also includes sex, race, comorbidity score, prior MDS, marital status, and geographic region DisclosuresSatram-Hoang:Genentech, Inc.: Consultancy. Hurst:Genentech, Inc.: Employment. Reyes:Genentech, Inc.: Employment.