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Abstract Background: To address the current and long-term unmet health needs of the growing population of patients diagnosed with non-Hodgkin lymphoma (NHL) and NHL survivors, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/) with recruitment at eight centers (Cornell University, Emory University, Mayo Clinic, MD Anderson, University of Iowa, University of Miami, University of Rochester, and Washington University). Here we report on the first two years of enrollment. Methods: All patients ≥18 years with NHL (exclusive of chronic lymphocytic leukemia) newly diagnosed within 183 days of enrollment are eligible. LEO reviews all pathology diagnoses; annotates and harmonizes all cases with clinical, epidemiologic, pathology and treatment data; builds a NHL tumor bank that includes an H&E slide, a formalin-fixed, paraffin-embedded tissue sample and extracted tumor DNA and RNA; collects peripheral blood and banks DNA, serum, plasma and buffy coat in a central biorepository; and prospectively follows patients regularly to ascertain disease progression/relapse, retreatment, transformation, second cancers, survival, updated exposures, and patient-reported outcomes. We examined the demographics, clinical characteristics, and NHL subtypes for patients enrolled in the first two years (2015-2017) in LEO Cohort and compared these characteristics to population-based registry data from the United States Surveillance, Epidemiology, and End Results (SEER) Program for 2011-2015. We also examined baseline body mass index (BMI), comorbidities and quality of life (QOL) measured with the Functional Assessment of Cancer Therapy - General (FACT-G) scores (0-100 scale) overall and by NHL subtype that were collected in LEO but not available in SEER or any other large, prospective US NHL cohort. Results: From 2015-2017, LEO enrolled 3244 NHL patients across 47 states (Figure) with a median age at diagnosis of 62 years (range, 18-94 years) and 55.9% male. Based on self-identified race/ethnicity, 85% of the participants were White, 7.4% were Black/African American, 2.6% were Asian, and 3.5% other or more than one race; 9.9% were Hispanic (regardless of race). The most common subtypes were diffuse large B-cell (DLBCL, 33.9%), follicular (FL, 22.2%), mantle cell (MCL, 9.3%), marginal zone (MZL, 8.3%), T-cell (TCL, 10.9%), and all other NHL subtypes (other NHL, 15.3%). These distributions were all within 5% of SEER data, except that 6.8% of participant in LEO were in the age 80+ years group compared to 18.5% in SEER (Table). The overall prevalence of self-reported comorbidities at enrollment was 13.5% for heart disease (range, 12.1% for FL to 16.6% for MCL), 8.8% for diabetes (range, 7.1% for FL to 9.8% for DLBCL), 4.6% for osteoporosis (range, 3.0% for MCL to 6.3% for MZL), and 5.9% for autoimmune disease (range, 2.0% for MCL to 12.6% for MZL). The median BMI was 27.9 kg/m2 (range, 26.7 kg/m2 for TCL to 28.3 kg/m2 for MZL). A total of 2151 participants completed the FACT-G with a median score of 78.7 (range, 75.9 for TCL to 83.3 for MZL). Complete IPI data were available on 2296 participants: 40.4% IPI 0-1, 32.7% IPI 2, 18.3% IPI 3, 8.7% IPI 4-5. Overall, 73.7% of participants were initially treated with systemic therapy (range, 40.0% for MZL and 91.8% for DLBCL); 14.5% were initially managed with observation (range, 1.4% DLBCL and 31.8% for FL); and 2.0% were initially manage with radiotherapy (range, 0.2% for MCL and 3.8% for DLBCL). Outcomes are being prospectively collected. Conclusions: LEO is a large and diverse cohort of newly diagnosed NHL patients and the first two years of enrollment reflects the broader United States NHL population. Enrollment is ongoing. This unique resource will enable examination of the interactions among a broad array of clinical and molecular factors and their impact on multiple outcomes, to improve prognostication, identify new approaches to improve outcomes and survivorship, and facilitate clinical trials using this infrastructure. Disclosures Flowers: Acerta: Research Funding; Pharmacyclics/ Janssen: Consultancy; OptumRx: Consultancy; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; Burroughs Wellcome Fund: Research Funding; Spectrum: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Abbvie: Consultancy, Research Funding; Bayer: Consultancy; Millennium/Takeda: Research Funding; Abbvie: Research Funding; Celgene: Research Funding; Gilead: Consultancy; BeiGene: Research Funding; Karyopharm: Consultancy; Denovo Biopharma: Consultancy; Genentech/Roche: Consultancy; Gilead: Research Funding; Genentech/Roche: Research Funding; National Cancer Institute: Research Funding; TG Therapeutics: Research Funding; Pharmacyclics: Research Funding. Nastoupil:TG Therappeutics: Research Funding; Merck: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Janssen: Research Funding; Spectrum: Honoraria; Gilead: Honoraria; Genentech: Honoraria, Research Funding; Karus: Research Funding; Novartis: Honoraria; Juno: Honoraria. Kahl:Acerta: Consultancy; Genentech: Consultancy; Seattle Genetics: Consultancy; Juno: Consultancy; CTI: Consultancy; ADC Therapeutics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Abbvie: Consultancy. Casulo:Celgene: Research Funding; Gilead: Honoraria. Friedberg:Bayer: Honoraria. Lossos:Affimed: Research Funding. Martin:Seattle Genetics: Consultancy; Kite: Consultancy; Janssen: Consultancy; Gilead: Consultancy; Bayer: Consultancy; AstraZeneca: Consultancy. Leonard:Bayer: Consultancy; Celgene: Consultancy; Biotest: Consultancy; United Therapeutics: Consultancy; BMS: Consultancy; MEI Pharma: Consultancy; Juno: Consultancy; Sutro: Consultancy; Karyopharm: Consultancy; ADC Therapeutics: Consultancy; Pfizer: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy; Genentech/Roche: Consultancy; Novartis: Consultancy. Bernal-Mizrachi:Kodikaz Therapeutic Solutions: Consultancy, Equity Ownership; Takeda Pharmaceutical Company: Research Funding. Maurer:Morphosys: Research Funding; Nanostring: Research Funding; Celgene: Research Funding. Cerhan:Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding.

Abstract Abstract 1563 Intro: Optimal treatment for non-follicular low grade B-cell lymphoma (NFLGL) is not well defined. Clinical trial design for these subtypes would be enhanced by further understanding of early presentation and management. A large prospective observational study was used to identify presenting clinical features, describe patterns of care and compare the prognostic utility of International Prognostic Index (IPI) and the Follicular Lymphoma International Prognostic Index (FLIPI). Methods: The University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) is a prospective, longitudinal observational study designed to collect information on patterns of care and outcomes for patients with newly diagnosed lymphoma. Patients seen at the Mayo Clinic, Rochester MN and the University of Iowa within 9 months of their initial diagnosis of lymphoma are offered enrollment. Baseline patient reported data, clinical data and initial management are collected using a standard protocol and a central pathology review is performed. After enrollment, patients are actively followed for events (disease progression, retreatment, and death) and disease related comorbidities. This analysis includes 198 patients diagnosed with extranodal marginal zone (EMZL), 22 with nodal marginal zone (NMZL), 47 with splenic marginal zone (SMZL), 48 with lymphoplasmacytic lymphoma (LPL), as well as 67 patients with unclassifiable low-grade B-cell lymphoma (B-NOS). Results: 382 newly diagnosed NFLGBL patients were enrolled in the MER from 2002–2009. The median age at enrollment was 63 years (range 22–95). 52% were male. The most common types of initial therapy after diagnosis were observation (41 %), alkylator- based chemotherapy +/− rituximab (R) (17%), radiation therapy alone (15%) and R monotherapy (13%). At median follow-up of 60 months (range 1–121), 51 patients were deceased and 155 had an event. The clinical characteristics that comprise the IPI and FLIPI were similar across the subtypes. Approximately half of the cases presented at low risk (55% IPI; 48% FLIPI). A majority had a normal LDH (81%), limited nodal involvement (91% had ≤ 4 groups), normal hemoglobin (71% ≥12 g/dL), good performance status (95% 0–1), and advanced stage (57% stages III-IV). There were notable differences among the NFLGBL subtypes. Patients with SMZL and LPL presented with high risk FLIPI of 3–5 (49% and 46% respectively) and high to high-intermediate IPI of 3–5 (21% and 13%). Abnormal LDH and anemia (< 12 g/dL) were more frequent in patients with SMZL (40%; 56%) and LPL (25%; 62%). EMZL presented with lower stages (70% stage I-II) and NMZL presented with increased nodal involvement (38% >4 sites). SMZL had the highest rates of surgical resection (21%) and EMZL was more frequently treated with radiation therapy (25%). LPL (30%) and NMZL (32%) more commonly received alkylator based chemotherapy +/− R. While both IPI and FLIPI differentiated outcome in these patients, FLIPI had higher c-statistics than IPI for both EFS (0.583 vs 0.568) and OS (0.685 vs 0.661), respectively. Conclusions: This large prospective collection of data on NFLGBL patients presenting to academic medical centers provides a modern picture of presentation and management patterns useful to investigators designing clinical trials. FLIPI may have more prognostic utility than IPI in this group of patients. Disclosures: Ansell: Seattle Genetics, Inc.: Research Funding.

This pilot study examined the functional impact of computerized versus compensatory calendar training in cognitive rehabilitation participants with mild cognitive impairment (MCI). Fifty-seven participants with amnestic MCI completed randomly assigned calendar or computer training. A standard care control group was used for comparison. Measures of adherence, memory-based activities of daily living (mADLs), and self-efficacy were completed. The calendar training group demonstrated significant improvement in mADLs compared to controls, while the computer training group did not. Calendar training may be more effective in improving mADLs than computerized intervention. However, this study highlights how behavioral trials with fewer than 30–50 participants per arm are likely underpowered, resulting in seemingly null findings.

ObjectiveThis study aims to provide effect size estimates of the impact of two cognitive rehabilitation interventions provided to patients with mild cognitive impairment: computerized brain fitness exercise and memory support system on support partners' outcomes of depression, anxiety, quality of life, and partner burden.MethodsA randomized controlled pilot trial was performed.ResultsAt 6 months, the partners from both treatment groups showed stable to improved depression and anxiety scores, while partners in an untreated control group showed worsening depression and anxiety over 6 months. There were no statistically significant differences on quality of life or burden outcomes in this small pilot trial; however, effect sizes were moderate, suggesting that the sample sizes in this pilot study were not adequate to detect statistical significance.ConclusionEither form of cognitive rehabilitation may help partners' mood or anxiety, compared with providing no treatment. However, effect size estimates related to other partner outcomes (i.e., burden, quality of life) suggest that follow‐up efficacy trials will need sample sizes of at least 30–100 people per group to accurately determine significance. Copyright © 2017 John Wiley & Sons, Ltd.