What Is the Impact on Quality of Life of Various Treatments in Acute Myeloid Leukemia and High-Risk Myelodysplastic Syndrome? Rethinking the Goals

Introduction: Treatment decisions are difficult for older patients with acute myeloid leukemia (AML) and high-risk myelodysplastic syndrome (MDS). Few studies address the impact of treatment on QOL. Both AML and high-risk MDS occur most frequently in the sixth and seventh decades of life, and are associated with a poor prognosis with median survival of one year or less. A primary goal of treatment is to improve quality of life (QOL) because cure is improbable. This was a longitudinal cohort study to compare QOL between groups receiving intensive therapy, non-intensive therapy, and supportive care. The sample consisted of 85 patients 60 years of age and older diagnosed high risk MDS and AML recruited from Moffitt Cancer Center from 12/2013 until 4/2015. Functional Assessment of Cancer Therapy-Leukemia (FACT-Leu) was used to measure QOL. Study aims were to: 1) To compare the difference in QOL scores measured by the Functional Assessment of Cancer Therapy-Leukemia version for intensive chemotherapy, non-intensive therapy and supportive care within 7 days of new treatment, or decision to pursue supportive care, and one month or later; 2) To determine QOL predictors of AML and high risk MDS from age, comorbidity, fatigue, and diagnosis; 3) To test the moderating effect of treatment with age, comorbidity, and fatigue on QOL. See figure 1. Methods: Recruitment of 85 patients with high risk MDS and AML occurred at the time of appointments in the Hematology Clinic or during admission to Moffitt Cancer Center for induction chemotherapy. Inclusion criteria included patients 60 years of age and older with confirmed diagnosis of high-risk MDS or AML based on bone marrow pathology report. High-risk MDS and AML were treatedas one group. Patients were able to read, write, and speak English, were orientedto person, place, and time, and werewilling to participate. Quality of life was assessedat the time of enrollment and within at least one month of enrollment using the FACT-Leu. Fatigue was measuredusing the Brief Fatigue Inventory, a one page, nine-item questionnaire, which measures fatigue on a scale of zero to ten, with zero indicating no fatigue, and ten representing the worst fatigue that a person can imagine. Measurement of number of comorbidities was performedat the time of enrollment using the Charlson comorbidity index. Baseline information obtained on all subjects included age, as measured by date of birth, and diagnosis from pathology report including chromosome analysis. Demographic data collected included gender, marital status, level of education, income level, religious ceremony attendance on a scale of zero to four, and designation of intensive, non-intensive, or supportive care treatment. Results: The first aim, a comparison of QOL scores from week 1 to week 4, was analyzed with repeated measures analysis of variance (ANOVA). The supportive care group was not included in the analysis because of low accrual. Results indicated that there was a significant group by time interaction (with p=.040). Follow up tests revealed that the intensive treatment group had a significant improvement in their QOL scores at 1 month post treatment (p=.020). The second aim, evaluation of predictors of QOL was conducted using Pearson's correlations with age, comorbidity, fatigue, and diagnosis with significant correlations found between fatigue and QOL (r=-.693, p< .001). These findings identify an important relationship between fatigue and QOL. This was a negative correlation, showing that as fatigue increased QOL decreased. The third aim was explored using regression with Hayes (2013) application for moderation analysis. Scores for QOL for age, comorbidity, and fatigue were not moderated by treatment. Conclusions: These findings suggest that the most intensive treatment approach improves QOL. In addition, fatigue is a significant predictor of QOL. As fatigue increased, QOL scores decreased. Additional studies with a larger, more diverse sample are needed to explore the relationship between treatment approaches and QOL. In addition, intervention studies can be developed in AML and high risk MDS focused on fatigue management. It is anticipated that the results of this study will be used to inform patients and health care providers when making decisions concerning treatment based on QOL outcomes. Figure 1. Figure 1. Figure 2. Figure 2. Disclosures Lancet: Seattle Genetics: Consultancy; Pfizer: Research Funding; Boehringer-Ingelheim: Consultancy; Kalo-Bios: Consultancy; Amgen: Consultancy; Celgene: Consultancy, Research Funding. Komrokji:Celgene: Consultancy, Research Funding; Incite: Consultancy; Novartis: Speakers Bureau; GSK: Research Funding. List:Celgene Corporation: Honoraria, Research Funding.