Hasselbalch, Hans Carl

Background: Quality of life (QOL) is predictive of survival in many malignancy types, including myeloproliferative neoplasms (MPNs; Scherber 2017, Sloan 2012, Montazeri 2009, Nilsson 2017). We have previously characterized that an association exists between symptom burden and QOL among MPN patients, but due to the disease specificity of symptoms, symptoms rather than QOL remains a key therapeutic endpoint (Scherber 2017, NCCN Guidelines). Despite these advancements, our understanding of the extent that different patient and disease characteristics, including symptoms, contribute to overall QOL has remained elusive. In this analysis, we utilized information from a large survey of MPN patients to develop a model of QOL that establishes the degree that individual variables contribute to QOL, including psychosocial variables, comorbidities, and MPN disease symptoms. Methods: The FATIGUE survey of MPN patients (Scherber 2016) investigated self-reported symptoms using the MPN10 (Scherber 2012), depression utilizing the Profile of Mood States-Brief (POMS-B, McNair 1971), Patient Health Questionnaire (PHQ-2, Kroenke 2003) and Mental Health Inventory (MHI-5, Berwick 1991), and QOL utilizing a single numeric analog scale (range 0-10) regarding overall quality of life. Linear regression analysis was utilized to establish the relationship between individual symptoms and QOL, and a structural equation model (SEM) was used to identify complex relationships among patient demographics, behavioral factors, comorbidities, and QOL. Results: A total of 914 patients from the online survey lived in the USA and provided data for this analysis. Average age was 62 with 67% of patients being female and the mean BMI was 25. Education varied across middle school or high school education (22%), undergraduate or college degree (44%), masters (26%), to doctorate (8%). 43% of respondents were employed. Fatigue (β coefficient 0.23, p<0.001), inactivity (β 0.21, p<0.001), concentration difficulties (β 0.13, p<0.001), sad mood (β 0.18, p<0.001), and night sweats (β 0.05, p=0.03) showed statistically significant impact on QOL. SEM Model: We developed the SEM model in Figure 1. Out of all variables analyzed, MPN total symptom burden demonstrated the strongest association with (β 0.89) with QOL, followed by depression (β 0.76). Comorbidities, including COPD and renal issues, age, and body mass index abnormalities had some impact on symptoms (all β <0.40), but did not demonstrate a significant impact on QOL. Comparative Fit Index (CFI) was 0.905 and root mean square error of approximation (RMSEA) was 0.051 (0.048, 0.054) indicating good fit. Conclusions: Previous clinical trials of JAK inhibition have targeted improvement in symptoms as a key endpoint, and ultimately demonstrated improvements in overall survival. The mechanism of this survival benefit has not been fully explored. This analysis suggests that symptoms and mood are strongly associated and potentially a major contributor to QOL among MPN patients, whereas other major comorbidities and age are not as strongly correlated. Efforts are underway to analyze more comprehensive datasets to better understand the role of other variables, including marriage status and financial concerns, on QOL. Disclosures Scherber: Blueprint: Other: Ad board; Incyte: Consultancy; Gilead: Consultancy. Hasselbalch:Novartis: Research Funding; AOP Orphan Pharmaceuticals: Other: Data monitoring board. Mesa:Baxalta: Consultancy; LaJolla: Consultancy; Genentech: Consultancy; Celgene Corporation: Research Funding; Samus: Research Funding; AbbVie: Research Funding; NS Pharma: Research Funding; Novartis: Consultancy, Honoraria, Other: travel, accommodations, expenses; CTI: Research Funding; Galena Biopharma: Consultancy; Pfizer: Research Funding; Incyte: Other: travel, accommodations, expenses, Research Funding; Genotech: Research Funding; AOP Orphan Pharmaceuticals: Honoraria, Other: travel, accommodations, expenses; PharmaEssentia: Research Funding; Gilead Sciences: Research Funding; Promedior: Research Funding; Shire: Honoraria; Sierra Oncology: Consultancy.

Blood eosinophilia (≥0.5 × 109/l) may be an early sign of hematological malignancy. We investigated associations between levels of blood eosinophils and risks of hematological malignancies and mortality in order to provide clinically derived cut‐offs for referral to specialist hematology care. From the Copenhagen Primary Care Differential Count (CopDiff) Database, we identified 356,196 individuals with at least one differential cell count encompassing the eosinophil count during 2000–2007 and matched these laboratory data with Danish nationwide health registers. We used multivariable logistic regression to calculate odds ratios (ORs) for the 4‐year incidences of hematological malignancies and mortality between the eosinophil counts and a reference count of 0.16 × 109/l which was the median eosinophil count in our data. Risks of hematological malignancies and mortality increased above the median eosinophil count. At the 99th percentile, corresponding to an eosinophil count of 0.75 × 109/l, risks of hematological malignancies were increased more than twofold with OR (95% C.I.) of 2.39 (1.91–2.99). Interestingly, risks reached a plateau around an eosinophil count of 1.0 × 109/l. Risks also increased when the eosinophil count approached zero. Here, counts associated relatively more with acute myeloid leukemia and myelodysplastic syndromes whereas counts above 0.16 × 109/l associated more with myeloproliferative neoplasms. Eosinophil counts associate with hematological malignancies and mortality even below the definition of eosinophilia. The observed plateau of risks around 1.0 × 109/l is important for physicians encountering patients with eosinophilia since even mild‐to‐moderate eosinophilia according to traditional definitions confers maximally increased risks of subsequent/subclinical hematological malignancy. Am. J. Hematol. 90:225–229, 2015. © 2014 Wiley Periodicals, Inc.