ALBERT

Description: With eight islands, complex coastlines and bottom topography, strong wind curls, and frequent upwelling fronts, the Southern California Bight (SCB) is an area with strong eddy activity. By applying an automated eddy detection scheme to a 12 year high-resolution numerical product of the oceanic circulation in the SCB, a three-dimensional eddy data set is developed. It includes information for each eddy's location, polarity, intensity, size, boundary, and moving path at nine vertical levels. Through a series of statistical analyses applied to the eddy data set, three-dimensional statistical characteristics of mesoscale and submeoscale eddy variations in the SCB are elucidated; these shed light on how eddies are generated, evolve, and terminate. A significant percentage of eddies is found to be generated around islands and headlands along the coastline, which indicates that islands in the SCB play a vital role in eddy generation. Three types of eddies, based on shape, are identified from the numerical product: bowl, lens, and cone. A dynamic analysis shows that some submesoscale eddies with finite local Rossby numbers tend to be ageostrophic balanced while mesoscale eddies are in geostrophic balance. The present research results are useful for the interpretation of data sets obtained during the interdisciplinary Santa Barbara Channel Radiance in a Dynamic Ocean (RaDyo) field experiment conducted on September 3–25, 2008.

Description: Biochemistry DOI: 10.1021/bi401492k

Description: BACKGROUND: DLBCL is the most common and a potentially curable non-Hodgkin lymphoma. Multiple previous studies have shown that minority populations have worse outcomes compared to Caucasians (Tao L, Blood 2014; Griffiths R, BMC Cancer 2010; Koroukian et al, Cancer, 2010, Shenoy PJ Cancer 2010). Moreover, it has been reported that uninsured and Medicaid insured patients with DLBCL have inferior survival compared to privately insured patients (Han X, Cancer 2014; Koroukian et al, Cancer 2010;). It has also been well established that minorities are underrepresented in clinical trials (Gerrero S, Sci Rep 2018; Kwiatkowski K, Cancer 2013). We present the baseline characteristics, treatment paradigms and outcomes of Caucasian (C) and non-Caucasian (NC) patients with de novo DLBCL treated at a single academic hybrid cancer center. METHODS: We collected demographic, disease, insurance coverage, treatment characteristics, and treatment outcomes for patients with de novo DLBCL who presented between January 2016 and January 2019 at Levine Cancer Institute, Charlotte, North Carolina. Patient race, C or NC were self-reported. Insurance was categorized as Government (Medicaid or Medicare), Private, or Uninsured. We used the Revised International Prognostic Index (R-IPI) to risk stratify patients. Double-hit lymphomas (DHL) were defined as having the MYC translocation with either BCL2 or BCL6 translocation on fluorescent in situ hybridization. Treatments included standard chemoimmunotherapies, stem cell transplantation and clinical trials including chimeric antigen receptor therapies (CART). Outcomes of overall survival (OS) and progression free survival (PFS) were calculated using the Kaplan Meier method and compared with log rank test. Demographic data was compared using Fisher's Exact tests. RESULTS: One hundred and ninety-six consecutive patients with de novo DLBCL were included in the analysis [155 (79%) = C, 41 (21%) = NC] (Table). The NC group was predominantly African American (71%) followed by Hispanic (15%). Prognostic scores (R-IPI) and the incidence of DHL were similar between C and NC. The median age at diagnosis in the NC group was lower than in C. There were significant differences in insurance coverage between the 2 groups (p=0.012). The C group did not have any uninsured patients and had more patients with private insurance (33%) compared to the NC group (7% uninsured and 27% with private insurance). The most common frontline treatment was RCHOP (C=66%, NC=70%) followed by dose adjusted REPOCH (C=12%, NC=15%). Median follow up was 31.6 months. There was no difference in OS and PFS between the 2 groups (Figure 1). OS at 2 years from date of diagnosis was 81% for C and 84% for NC, p=0.852. Two-year PFS from time of diagnosis were similar for both groups: 61% for C and 63% for NC, p=0.999. Similar numbers of patients in both groups developed relapsed or refractory (R/R) disease after frontline therapy. Median number of treatments was 2 for both groups, p=0.582. For patients who developed R/R DLBCL, the 2-year OS was 60% for C and 63% for NC, p=0.590. Similar proportions underwent stem cell transplantation: 11% for C and 20% for NC, p= 0.186. Clinical trial enrollment was comparable: 11% for C and 12% for NC, p=0.785. CONCLUSION: Unlike previous population-based studies that have shown racial disparities with superior outcomes for Caucasians and for patients with private insurance, our single center experience demonstrates similar survival outcomes between Caucasians and non-Caucasians diagnosed with de novo DLBCL, despite differences in insurance coverage favoring Caucasians. In the R/R setting, similar proportions of both groups underwent stem cell transplantation and enrolled on clinical trials. The likely explanation is that our safety net cancer center, with extensive nurse navigator support and access to standard treatments, stem cell transplants and cutting-edge clinical trials may abrogate the inferior outcomes in minority populations that have been previously reported. Disclosures Symanowski: Immatics: Consultancy; Eli Lilly: Consultancy; Carsgen Therapeutics: Consultancy; Boston Biomedical: Consultancy. Park:Rafael Pharma: Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Research Funding; Gilead: Speakers Bureau; Teva: Consultancy, Research Funding; G1 Therapeutics: Consultancy; Seattle Genetics: Research Funding, Speakers Bureau. Avalos:Juno: Membership on an entity's Board of Directors or advisory committees; Best Practice-Br Med J: Patents & Royalties: receives royalties from a coauthored article on evaluation of neutropenia. Jacobs:Genentech: Speakers Bureau; AstraZeneca: Speakers Bureau; TG Therapeutics: Honoraria, Research Funding; AbbVie: Consultancy, Speakers Bureau; JUNO: Consultancy; Gilead: Consultancy; Pharmacyclics LLC, an AbbVie Company: Research Funding, Speakers Bureau. Ghosh:TG Therapeutics: Consultancy, Honoraria, Research Funding; SGN: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Speakers Bureau; Gilead: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Pharmacyclics LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Research Funding; Genentech: Research Funding; AstraZeneca: Honoraria, Speakers Bureau; Forty Seven Inc: Research Funding.

Description: Background: Financial toxicity (FT) has been consistently demonstrated to a be a major contributor to morbidity and mortality in a variety of cancers. However, the vast majority of research examining this issue has been in solid tumors, and there has been less investigation of how this concept applies in malignant hematology and even fewer studies looking at an interventional model. This pilot study attempts to identify patients at high-risk due to FT in a busy clinical environment and improve clinical outcomes with comprehensive intervention. Methods: All patients seen at the Malignant Hematology Clinic at the Levine Cancer Institute, a tertiary hospital-based specialty practice, were surveyed at their visits over a six-month period. All patients were aged ≥18 years and diagnosed with hematologic malignancy or bone marrow failure syndrome. The survey consisted of the PROMIS Global-10 measure and two questions from the COST measure. FT was defined as scoring 5 or less (maximum: 10) in agreement with the COST questions: "I know that I have enough money in savings, retirement, or assets to cover the costs of my treatment" and "I am satisfied with my current financial situation." Patients with FT were entered into the interventional cohort and scheduled for a visit with a nurse navigator where they completed a standardized worksheet to identify gaps in care and opportunities for grant funding/other assistance. Patients were seen by a clinical pharmacist for copay review and discussion of assistance programs. Finally, patients were offered the services of a community pro-bono financial planner for help with budgeting, asset management, and general financial advice. Patients were tracked longitudinally for assistance provided, changes in PROMIS scores, and clinical outcomes. Categorical variables, including responses to survey questions, were summarized with frequencies and proportions, while continuous variables were summarized with medians and ranges. Correlation of FT screening scores and COST scores was assessed with Spearman's correlation. Baseline versus post-intervention PROMIS scores were compared with paired t-tests, while McNemar tests for agreement were used to compare ER and IP utilization 3 months prior versus post intervention. Results: A total of 107 patients were included in the intervention. Specific characteristics of the intervention population are listed in Table 1. FT screening scores were found to correlate with the full COST measure (Spearman correlation = 0.45, p

Description: Background: Financial toxicity (FT) is defined as financial distress as a result of disease or treatment decisions. FT might be considered analogous to physical toxicity. Patients with high risk hematologic malignancies represent an especially vulnerable group for cost related issues due to heavy healthcare utilization, high costs associated with their treatment, and the potentially deadly consequences of treatment noncompliance. We hypothesized that comprehensive intervention on the financial aspects of care for these patients would lead to decreased mortality. Methods: All patients seen in Leukemia Clinic at the Levine Cancer Institute, a tertiary hospital-based specialty practice, were surveyed prior to their visit over a six-month period. All patients were aged ≥18 years and diagnosed with a high-risk hematologic malignancy (intermediate/high risk AML, high risk ALL, high or very high risk MDS, blast phase CML, or mixed phenotypic acute leukemia). The survey consisted of the PROMIS Global-10 measure and two questions from the COST measure. FT was defined as scoring 5 or less (maximum: 10) in agreement with the COST questions: "I know that I have enough money in savings, retirement, or assets to cover the costs of my treatment" and "I am satisfied with my current financial situation." Patients who met these criteria were entered into the interventional cohort and scheduled for comprehensive intervention based on availability of study staff. Scheduling was done anonymously and without prioritization of perceived need. The intervention consisted of a nurse navigator visit where patients completed a standardized worksheet to identify gaps in care and opportunities for grant funding/other assistance, a clinical pharmacist visit for medication copay review and discussion of assistance programs, and the services of a community pro-bono financial planner for assistance with budgeting, asset management, and general financial advice. Demographic/clinical data were abstracted from the medical record and patients were tracked longitudinally for clinical outcomes. Categorical factors were summarized with proportions and compared between groups with Fisher's exact tests, while continuous factors were summarized with medians and compared between groups with median two-sample tests. Overall survival (OS) was evaluated with Kaplan Meier methods and compared between groups with a log rank test. Cox proportional hazards models were utilized in model selection procedures for OS. Individual prognostics were identified with univariable models, then backward elimination and forward selection (entry/elimination criteria p = 0.10) were carried out to identify a final base model. This was utilized to estimate an adjusted HR for the intervention variable. Results: A total of 105 patients met criteria and were placed in the interventional cohort with the intention to receive the full intervention. Of these patients, 59 (56.2%) were able to be scheduled and receive the full intervention, while the remainder received standard care only. There were no significant differences between the groups when compared by gender, race, age, marital status, insurance type, FT screening score, therapy received, or disease type (Table 1). OS was significantly different between the two groups, with a mortality rate during the study period of only 27% for the patients that received the full FT intervention as opposed to 43% for the patients who received standard care. Adjusted OS at 6 months for the interventional cohort was 81.4% (95% CI 68.9%-89.2%) versus 73.9% (95% CI 58.7%-84.3%) for the non-intervention group; OS rates at 12 months were 73.0% (59.0%-82.9%) and 46.4% (28.9%-63.8%), respectively (Figure 1). On univariate analysis, intervention was significantly associated with survival (HR: 0.44, 95% CI: 0.22 - 0.86, p = 0.017). After adjusting for insurance, race, and age at survey, risk of death in those receiving the intervention was 0.47 times the risk of death in those without the intervention (95% CI: 0.23 - 0.98, p = 0.043). Conclusions: High risk hematologic malignancy patients are at high risk for increased complications due to financial concerns. Intervening on FT in a comprehensive way including navigators, pharmacists, and financial counselors is effective and leads to decreased mortality. Disclosures Knight: Foundation for Financial Planning: Research Funding. Ai:Incyte: Speakers Bureau; Celgene: Speakers Bureau. Chojecki:Incyte: Research Funding; Novartis: Other: Investigator Meeting Attendance. Copelan:Amgen: Membership on an entity's Board of Directors or advisory committees. Grunwald:Pfizer: Consultancy; Agios: Consultancy; Abbvie: Consultancy; Agios: Consultancy; Daiichi Sankyo: Consultancy; Agios: Consultancy; Cardinal Health: Consultancy; Incyte: Consultancy, Research Funding; Trovagene: Consultancy; Daiichi Sankyo: Consultancy; Astellas: Consultancy; Daiichi Sankyo: Consultancy; Trovagene: Consultancy; Abbvie: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Merck: Consultancy; Abbvie: Consultancy; Celgene: Consultancy; Janssen: Research Funding; Trovagene: Consultancy; Premier: Consultancy; Astellas: Consultancy; Astellas: Consultancy; Genentech/Roche: Research Funding; Premier: Consultancy; Premier: Consultancy; Amgen: Consultancy; Cardinal Health: Consultancy; Incyte: Consultancy, Research Funding; Celgene: Consultancy; Genentech/Roche: Research Funding; Genentech/Roche: Research Funding; Forma Therapeutics: Research Funding; Incyte: Consultancy, Research Funding; Merck: Research Funding; Janssen: Research Funding; Forma Therapeutics: Research Funding; Forma Therapeutics: Research Funding; Pfizer: Consultancy; Pfizer: Consultancy; Merck: Consultancy; Amgen: Consultancy; Merck: Consultancy; Cardinal Health: Consultancy.