Determining Treatment for Older Patients with High-Risk MDS and AML: Which Factors Predict Optimal Quality of Life?
Authors: Tinsley-Vance Sara M. , Small Brent J , McMillan Susan C , Komrokji Rami S. , Lancet Jeffrey E. 
Published: October 2019

CONTEXT: Evidence is lacking to guide patients diagnosed with acute myeloid leukemia and high-risk myelodysplastic syndrome in selecting the best treatment for improving quality of life. A model is needed to determine best choice for improving quality of life based on individual characteristics including age, comorbidities, and functional status.

OBJECTIVE: The aim of this study was to determine whether age, comorbidity, or fatigue, had a moderating effect on quality of life at two different intensities of therapy; intense or non-intense. Intense therapy included any induction chemotherapy which required an inpatient hospitalization. Non-intense chemotherapy was active disease treatment in an outpatient setting, and included hypomethylating agents and clinical trials.

DESIGN: The study design was an exploratory design with repeated measures comparing quality of life between two treatment groups, intensive and non-intensive prior to start of new treatment and again one month post treatment. Enrollment of participants began in 12/2013, and ended 3/2015. Patients were followed for one month. Linear regression was performed to determine the moderating effect of treatment on age, comorbidity, and fatigue on the second quality of life score.

SETTING: The setting was Moffitt Cancer Center. Participants were recruited in both the inpatient and outpatient settings.

PATIENTS OR OTHER PARTICIPANTS: Patients with pathology confirmed high risk myelodysplastic syndrome and acute myeloid leukemia, 60 years of age and older who were starting a new treatment were approached for participation. Recruitment of 85 patients occurred at the time of appointment in the Hematology Clinic or during admission to Moffitt Cancer Center for treatment. Patients were able to read, write, and speak English, were orientedto person, place, and time, and werewilling to participate. High-risk MDS and AML were treatedas one group. The sample was predominantly white, male, retired, and middle class. Seventy-nine percent of participants completed both quality of life measurements.

INTERVENTIONS: This was not an intervention study. Quality of life was measured in individuals with a focus on individual variables to see if the variables were correlated with a different quality of life score based on intensity of therapy.

MAIN OUTCOMES MEASURES: The main outcome of this study was quality of life, measured using the Functional Assessment of Cancer Therapy-Leukemia version. This was measured at two times with the second measurement used in the regression analysis. Comorbities, fatigue and age were entered into regression, with intensity of treatment used as a moderator variable. Comorbities was calculated with the Charlson comorbidity index. Fatigue was measured with the Brief Fatigue Inventory. Age was measured in years.

RESULTS: Linear regression was performed to determine the moderating effect of treatment on age, comorbidity, and fatigue on the second QOL score. The results were statistically significant, indicating that when combining all three variables, the model predicts QOL (p=0.049). Type of treatment was significant (p=.043). This indicates the main effects of treatment on QOL scores. The intensive treatment group had an improvement in the second measure of quality of life. Charlson comorbidity index and age did not moderate the quality of life score for intensity of treatment. The main effect of fatigue was significant (p = .014).

CONCLUSIONS: Intensive chemotherapy, regardless of age and comorbidity, was associated with improved quality of life score at one month. Age was not helpful in discriminating between intensity of therapy with quality of life as an outcome, at the one month mark. A larger sample size with more diverse demographic characteristics would enhance future studies.

This study was supported by an American Cancer Society Doctoral Degree in Nursing Science Scholarship

Disclosures

Tinsley-Vance: Ariad: Consultancy; Celgene: Speakers Bureau; Teva: Membership on an entity's Board of Directors or advisory committees; Novartis: Speakers Bureau. Komrokji:Novartis: Consultancy, Speakers Bureau; Boehringer-Ingelheim: Research Funding; Incyte: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding.