Coping Strategies in Patients with Acute Myeloid Leukemia

Patients diagnosed with acute myeloid leukemia (AML) face sudden-onset life-threatening disease that requires intensive treatments. Although their early disease trajectory is characterized by significant, toxic side effects, there is limited data describing coping strategies among patients with AML and how these inform patient-reported outcomes. We used cross-sectional secondary data analyses to describe coping in 160 patients with newly diagnosed high-risk AML. We used the Brief COPE, Hospital Anxiety and Depression Scale, PTSD Checklist-Civilian Version, and Functional Assessment of Cancer Therapy-Leukemia at time of AML diagnosis to measure coping strategies, psychological distress and quality of life (QOL), respectively. We used the median split method for distribution of coping domains, and multivariate regression models to assess the relationship between coping and patient-reported outcomes. Participants (median age=64.4 years) were mostly non-Hispanic White (86.3%), male (60.0%), and married (73.8%). Most (51.9%) had high utilization of approach-oriented coping strategies whereas 38.8% had high utilization of avoidant coping strategies. At time of diagnosis, use of approach-oriented coping was associated with less psychological distress (anxiety: β=-0.262, p=0.002; depression symptoms β=-0.311, p<0.001; PTSD symptoms: β=-0.596, p=0.006) and better QOL (β=1.491, p=0.003). Use of avoidant coping was associated with more psychological distress (anxiety: β=0.884, p<0.001; depression symptoms: β=0.697, p<0.001; PTSD symptoms: β=3.048, p<0.001) and worse QOL (β=-5.696, p<0.001). Patients with high-risk AML utilize various approach-oriented and avoidant coping strategies at time of diagnosis. Use of approach-oriented coping strategies was associated with less psychological distress and better QOL, suggesting a possible target for supportive oncology interventions.

The Associations between Coping Strategy Use and Patient-Reported Outcomes in Patients with Acute Myeloid Leukemia

Background: Patients with acute myeloid leukemia (AML) who receive intensive chemotherapy must cope with immense physical and psychological symptoms associated with a variety of patient-reported outcomes (PROs) such as quality of life (QOL). Although coping is critical to the management of an AML diagnosis and its treatment, data characterizing the use of coping strategies and its associations with PROs in the AML population are limited. Hence, we characterize coping strategy use among patients with AML and examine the associations between coping strategy use, psychological distress, and QOL. Methods: We used cross-sectional secondary data analyses to describe coping in 160 patients with newly diagnosed high-risk AML enrolled in a multi-site randomized supportive care trial. We used the Brief COPE, Hospital Anxiety and Depression Scale (HADS), PTSD Checklist-Civilian Version (PCL-C), and Functional Assessment of Cancer Therapy-Leukemia (FACT-Leu) within 72 hours of patient initiation of chemotherapy, to measure coping strategies, psychological distress and QOL, respectively. We grouped coping strategies into two higher-order domains of coping based on prior literature: approach-oriented coping (i.e., use of emotional support, active coping, positive reframing, acceptance) or avoidant coping (i.e., self-blame, denial, behavioral disengagement). We used the median split method for the distribution of coping domains. We used multivariate regression models adjusting for age, gender and diagnosis type (newly diagnosed vs. relapsed/refractory AML) to assess the relationship between coping and PROs. Results: Participants (median age of 64.4 years) were mostly non-Hispanic White (86.3%), male (60.0%), and married (73.8%). Most (51.9%) reported high utilization of approach-oriented coping strategies (e.g., emotional support) whereas 38.8% reported high utilization of avoidant coping strategies (e.g., denial) (Figure 1). At the time of AML diagnosis, use of approach-oriented coping was associated with less psychological distress and better QOL (Table 1). Use of avoidant coping was associated with more psychological distress and worse QOL. Additionally, patients who used multiple approach-oriented coping strategies had less psychological distress and better QOL (Table 2). In contrast, patients who used multiple avoidant coping strategies had more psychological distress, and worse QOL. Conclusions: Our study illustrates that most patients with high-risk AML utilize both approach-oriented and avoidant coping strategies. Our results also reveal links between approach-oriented coping strategies, less psychological distress, and better QOL. These findings underscore the need for early integration of supportive oncology interventions that help patients to cultivate approach-oriented coping strategies. Figure 1 Figure 1. Disclosures Brunner: Novartis: Consultancy, Research Funding; BMS/Celgene: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Acceleron: Consultancy; Agios: Consultancy; Keros Therapeutics: Consultancy; GSK: Research Funding; Aprea: Research Funding; AstraZeneca: Research Funding; Janssen: Research Funding. Fathi: AbbVie: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Blueprint: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Daiichi Sankyo: Consultancy, Honoraria; Genentech: Consultancy, Honoraria; Trillium: Consultancy, Honoraria; Kura: Consultancy, Honoraria; Foghorn: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Ipsen: Consultancy, Honoraria; Agios: Consultancy, Honoraria, Research Funding; Servier: Research Funding; Celgene/BMS: Consultancy, Honoraria, Research Funding. LeBlanc: Seattle Genetics: Consultancy, Other: Advisory board, Research Funding; Pfizer: Consultancy, Other: Advisory Board; AstraZeneca: Consultancy, Honoraria, Other: Advisory board, Research Funding; UpToDate: Patents & Royalties; American Cancer Society: Research Funding; Agios: Consultancy, Honoraria, Other: Advisory board; Travel fees, Speakers Bureau; BMS/Celgene: Consultancy, Honoraria, Other: Travel fees, Research Funding, Speakers Bureau; Daiichi-Sankyo: Consultancy, Honoraria, Other: Advisory board; Flatiron: Consultancy, Other: Advisory board; Astellas: Consultancy, Honoraria, Other: Advisory board; AbbVie: Consultancy, Honoraria, Other: Advisory board; Travel fees, Speakers Bureau; Otsuka: Consultancy, Honoraria, Other; Jazz Pharmaceuticals: Research Funding; Duke University: Research Funding; Helsinn: Consultancy, Research Funding; Heron: Consultancy, Honoraria, Other: advisory board; CareVive: Consultancy, Other, Research Funding; NINR/NIH: Research Funding; Amgen: Consultancy, Other: travel.

Simple Synthesis of Ru Decahedral Hollow Nanocages with Face-Centered Cubic Structure

Metallic nanocrystals with specific morphologies are of great interest to various applications, in particular for nanocages with well-defined and controllable surface due to high surface-to-volume ratio with high utilization efficiency of atoms. In the present work, Ru decahedral nanocages were synthesized via a combination of seed-mediated growth and chemical etching approach over Pd decahedra seeds. To be specific, the Pd decahedra were synthesized via a standard procedure, on which the Ru out layers were grown by seed-mediated growth with a few nanometers. Subsequently, Ru decahedral nanocages were formed with selective chemical etching of Pd cores in acidic aqueous solution. The present work suggests an effective strategy towards synthesis of hollow nanocages.

Mining Profitable and Concise Patterns in Large-Scale Internet of Things Environments

In recent years, HUIM (or a.k.a. high-utility itemset mining) can be seen as investigated in an extensive manner and studied in many applications especially in basket-market analysis and its relevant applications. Since current basket-market scenario also involves IoT equipment to collect information, i.e., sensor or smart devices, it is necessary to consider the mining of HUIs (or a.k.a. high-utility itemsets) in a large-scale database especially with IoT situations. First, a GA-based MapReduce model is presented in this work known as GMR-Miner for mining closed patterns with high utilization in large-scale databases. The k -means model is initially adopted to group transactions regarding their relevant correlation based on the frequency factor. A genetic algorithm (GA) is utilized in the developed MapReduce framework that can be used to explore the potential and possible candidates in a limited time. Also, the developed 3-tier MapReduce model can be easily deployed in Spark for the handlings of any database of large scale for knowledge discovery of closed patterns with high utilization. We created sets of extensive experimental environments for evaluating the results of the developed GMR-Miner compared to the well-known and state-of-the-art CLS-Miner. We present our in-depth results to show that the developed GMR-Miner outperforms CLS-Miner in many criteria, i.e., memory usage, scalability, and runtime.