Saliba, Antoine N.

Abstract Introduction: Mantle cell lymphoma (MCL) is an uncommon hematological malignancy with an estimated incidence of 1 per 100,000 persons per year in the United States and represents only about 5% of all non-Hodgkin lymphomas. Several studies have shown that treatment at academic centers and a higher hospital case volume are associated with improved outcomes for uncommon hematological malignancies, probably due to increased provider expertise and access to novel therapies. Treatment of MCL can be complex given the heterogenous nature of the disease and a frequent need for autologous stem cell transplantation in eligible patients. However, the impact of treatment at an academic center and facility patient volume on the survival of patients with MCL has not been well studied in large cohorts. In this study, we utilized the National Cancer Database (NCDB) to investigate the impact of treatment at an academic center and treatment facility volume on the overall survival (OS) of patients with MCL. Methods: The NCDB was used to identify adult patients (≥ 18 years) with newly diagnosed MCL from 2004 through 2017. For facility patient volume analysis, patients were divided into groups based on the average number of new MCL patients seen annually: Tercile 1 [T1] (1-3 patients/year), Tercile 2 [T2] (4-5 patients/year) and Tercile 3 [T3] (≥6 patients/year). Treating centers were divided into Academic and Non-academic using the NCDB definitions. Academic centers were defined as centers that accessions more than 500 newly diagnosed cancer cases per year, participate in postgraduate medical education in at least four program areas including internal medicine and surgery and participates in cancer-related clinical trials. The primary endpoint was overall survival (OS). Survival analysis was performed using the Kaplan-Meier method and Cox hazards proportional model. Statistical analysis was performed using SPSS version 25. Results: We identified 22,752 patients with MCL during the study period. 9,484 (42%) patients were treated at academic centers and 13,070 (57%) were treated at non-academic centers. In terms of facility patient volume 10,948 patients (48%) were in the T1 group, 4,637 (20%) were in the T2 group and 7,166 (31%) were in the T3 group. No significant differences were found in baseline demographics (age, gender, race/ethnicity, comorbidity scores), socioeconomical variables (insurance type, median income, area of residence) and disease-related factors (B-symptoms, Ann Arbor stage) between patients treated academic vs nonacademic centers, or between patients in T1 vs T2 vs T3 groups. Notably, compared to lower volume facilities, T3 facilities were more likely to be academic centers (T3: 81% vs T2: 42% vs T1: 16%, p<0.001) . After a median follow-up of 3.4 years, the median overall survival (OS) was 5.6 years for the entire cohort. The median OS was inferior for patients treated at lower volume facilities (4.1 years for T1, 5.1 years for T2 and 9.0 years for T3, p<0.001) (Figure 1A). Similarly, the median OS was shorter for patients treated at non-academic centers vs academic centers (4.3 years vs 7.5 years respectively, p<0.001) (Figure 1B). In a multivariate analysis, treatment at a lower patient volume facility (Hazard ratio [HR] Q1= 1.26 [95%CI = 1.18-1.34]) and treatment at a non-academic center (HR = 1.1, 95%CI = 1.01-1.12) were both independent prognostic factors of inferior OS, after adjusting for demographics (age, gender, ethnicity, area of residence) and socioeconomic variables (income and insurance status). Conclusion: Patients with MCL treated at academic and higher volume facilities had a higher OS compared to patients treated at non-academic and lower volume facilities.. Additional research is needed to fully understand the mechanisms behind these differences. Patients with MCL may benefit from an early referral to academic and high-volume centers. Figure 1 Figure 1. Disclosures Munoz: Merck: Research Funding; Portola: Research Funding; Genentech: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Seattle Genetics: Research Funding; Pharmacyclics/Abbvie, Bayer, Gilead/Kite Pharma, Pfizer, Janssen, Juno/Celgene, BMS, Kyowa, Alexion, Beigene, Fosunkite, Innovent, Seattle Genetics, Debiopharm, Karyopharm, Genmab, ADC Therapeutics, Epizyme, Beigene, Servier: Consultancy; Gilead/Kite Pharma, Kyowa, Bayer, Pharmacyclics/Janssen, Seattle Genetics, Acrotech/Aurobindo, Beigene, Verastem, AstraZeneca, Celgene/BMS, Genentech/Roche.: Speakers Bureau; Millennium: Research Funding; Pharmacyclics: Research Funding; Celgene: Research Funding; Physicians' Education Resource: Honoraria; Gilead/Kite Pharma: Research Funding; Kyowa: Honoraria; Bayer: Research Funding; Seattle Genetics: Honoraria; OncView: Honoraria; Targeted Oncology: Honoraria. Paludo: Karyopharm: Research Funding. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Nowakowski: Daiichi Sankyo: Consultancy; Zai Labolatory: Consultancy; TG Therapeutics: Consultancy; Blueprint Medicines: Consultancy; Nanostrings: Research Funding; MorphoSys: Consultancy; Kymera Therapeutics: Consultancy; Incyte: Consultancy; Ryvu Therapeutics: Consultancy; Kyte Pharma: Consultancy; Genentech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/Bristol Myers Squibb: Consultancy, Research Funding; Selvita: Consultancy; Curis: Consultancy; Karyopharm Therapeutics: Consultancy; Bantham Pharmaceutical: Consultancy. Wang: Novartis: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; InnoCare: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding.

PURPOSE: The benefit of routine pre-emptive screening for severe acute respiratory syndrome coronavirus 2 infections in patients with cancer before cancer-directed therapies is unclear. Herein, we characterize the outcomes of a cohort of patients with cancer who were diagnosed with COVID-19 by routine screening (RS) in comparison with those diagnosed on the basis of clinical suspicion or exposure history (nonroutine screening [NRS]). METHODS: A multisite prospective observational study was conducted at three major and five satellite campuses of the Mayo Clinic Cancer Center between March 18 and July 31, 2020. The primary outcome was COVID-19–related hospital admission. Secondary outcomes included intensive care unit admissions and all-cause mortality. RESULTS: Five thousand four hundred fifty-two patients underwent RS in the outpatient setting only, and 44 (0.81%) were diagnosed with COVID-19. RS detected 19 additional patients from the scheduled inpatient admissions for surgical or interventional procedures or inpatient chemotherapy. One hundred sixty-one patients were diagnosed with COVID-19 on the basis of NRS. COVID-19–related hospitalization rate (17.5% v 26.7%; P = .14), intensive care unit admission (1.6% v 5.6%; P = .19), and mortality (4.8% v 3.7%; P = .72) were not significantly different between the RS and NRS groups. In the multivariable analysis, age ≥ 60 years (odds ratio, 4.4; P = .023) and an absolute lymphocyte count ≤ 1.4 × 109/L (odds ratio, 9.2; P = .002) were independent predictors of COVID-19–related hospital admission. CONCLUSION: The COVID-19 positivity rate was low on the basis of RS. Comparing the hospital admission and mortality outcomes with the NRS cohort, there were no significant differences. The value of routine pre-emptive screening of asymptomatic patients with cancer for COVID-19 remains low.

PURPOSE: The goal of this study was to assess the impact of an interdisciplinary remote patient monitoring (RPM) program on clinical outcomes and acute care utilization in cancer patients with COVID-19. METHODS: This is a cross-sectional analysis following a prospective observational study performed at Mayo Clinic Cancer Center. Adult patients receiving cancer-directed therapy or in recent remission on active surveillance with polymerase chain reaction–confirmed SARS-CoV-2 infection between March 18 and July 31, 2020, were included. RPM was composed of in-home technology to assess symptoms and physiologic data with centralized nursing and physician oversight. RESULTS: During the study timeframe, 224 patients with cancer were diagnosed with COVID-19. Of the 187 patients (83%) initially managed in the outpatient setting, those who did not receive RPM were significantly more likely to experience hospitalization than those receiving RPM. Following balancing of patient characteristics by inverse propensity score weighting, rates of hospitalization for RPM and non-RPM patients were 2.8% and 13%, respectively, implying that the use of RPM was associated with a 78% relative risk reduction in hospital admission rate (95% CI, 54 to 102; P = .002). Furthermore, when hospitalized, these patients experienced a shorter length of stay and fewer prolonged hospitalizations, intensive care unit admissions, and deaths, although these trends did not reach statistical significance. CONCLUSION: The use of RPM and a centralized virtual care team was associated with a reduction in hospital admission rate and lower overall acute care resource utilization among cancer patients with COVID-19.

7542 Background: 60-70% of patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) can be cured with R-CHOP or R-CHOP-like immunochemotherapy. However, patients ≥80 years of age were either excluded or underrepresented in modern DLBCL trials, and their outcomes are understudied. The aim of this study is to define the survival trends and risk factors for inferior survival in older adult patients with DLBCL. Methods: Patients with newly diagnosed DLBCL were identified from the National Cancer Database (2004-2017, representing the rituximab era). Clinical characteristics, treatment, and outcomes were compared between patients ages ≥ 80, 65-79, and < 65 years. The Kaplan-Meier method and Cox proportional hazards model were used for survival analysis. Results: A total of 231,756 patients with newly diagnosed DLBCL were identified; 46,250 (20%) were ≥80 years, 87,702 (38%) were 65-79 years, and 97,904 (42%) were < 65 years. Patients ≥80 years were more likely to have a higher Charlson-Deyo Comorbidity Index score (CDS) (CDS ≥2, 12% vs 11% vs 8%, p = 0.001), less likely to receive systemic chemotherapy (63% vs 83% vs 89%, p < 0.001), and more likely to receive treatment at a non-academic center (71% vs 65% vs 48%, p < 0.001), compared to patients 65-79 and < 65 years, respectively. Median overall survival (OS) was significantly worse for patients ≥80 years compared to patients 65-79 years (11.6 vs 61.0 months, p = 0.001) and patients < 65 years (11.6 vs 178.1 months, p = 0.001). During the study period, the median OS had only minimally improved for patients ≥80 years (10.6 months in 2004-2007 vs 11.5 months in 2008-2011 vs 12.3 months in 2012-2016, p = 0.006). In contrast, the OS improvement appears more meaningful in patients 65-79 years (median in months: 51 vs 61.2 vs 65.9, p = < 0.001) and patients < 65 years (median in years: 14.6 vs 11.3 vs not reached, p < 0.001) in the prespecified intervals (2004-07, 2008-11, and 2012-16). In multivariate analysis, the most substantial risk factor for worse survival in patients ≥80 years was not receiving systemic therapy (hazard ratio [HR] = 3.26, 95%CI = 3.01-3.54, p = 0.001). Other risk factors associated with worse survival included high-risk IPI score (HR = 2.16, 95%CI = 1.96-2.39, p = 0.001), CDS score ≥2 (HR = 1.56, 95%CI = 1.40-1.73, p = 0.001), male sex (HR = 1.16, 95%CI = 1.09-1.24, p = 0.001), B symptoms at diagnosis (HR = 1.16, 95%CI = 1.08-1.25, p = 0.001), and treatment at a non-academic center (HR = 1.1, 95%CI = 1.01-1.20, p = 0.001). Conclusions: Patients ≥ 80 years of age with DLBCL have a significantly inferior survival which has not meaningfully improved in recent years. More than 1/3 of patients ≥ 80 years did not receive systemic therapy. Older adult patients with DLBCL should be assessed for fitness for chemotherapy using validated geriatric assessment tools. Novel therapeutic strategies with favorable safety profiles are urgently needed for this expanding patient population.

6539 Background: The impact of insurance status on survival in diffuse large B‐cell lymphoma (DLBCL), the most common aggressive lymphoma, has not been evaluated after the implementation of the Affordable Care Act (ACA). The aim of this study is to compare overall survival (OS) in patients across insurance status groups and in the periods before and after the ACA. Methods: Adult patients with newly diagnosed DLBCL were identified from the National Cancer Database. The analysis was restricted to patients 64 years of age or younger as most patients 65 years or older are eligible for Medicare under the ACA. The 2004-2017 period was chosen to represent the immunochemotherapy era preceding and following the ACA. Logistic regression was used to explore associations between abstracted variables and insurance status groups. The Kaplan-Meier method and Cox proportional hazards model were used for survival analysis. Results: 93,692 adults (age < 64 years) with newly diagnosed DLBCL and known insurance status were identified (41.3% female, median age 54 years [range: 18 – 64], 81.8% White and 12.1% Black). 7,211 (7.7%) patients were uninsured, 64,744 (69.1%) had private insurance, 11,936 (12.7%) had Medicaid, and 9,801 (10.5%) had Medicare. When compared to insured patients (private insurance, Medicaid or Medicare), uninsured patients were more likely to have a median household outcome of < $38,000 [OR 1.93 (95% CI 1.79-2.07)], less likely to receive chemotherapy [OR 0.69 (0.64-0.77)], more likely to be male [OR 1.14 (1.07-1.21)], more likely to be non-White [OR 1.30 (1.20-1.40], and more likely to present with stage III or IV disease [OR 1.24 (1.16-1.32)]. Uninsured patients had an inferior OS [HR 1.21 (95% CI 1.15-1.27)] when compared to insured patients after adjustment for baseline comorbidity (Charlson-Deyo score ≥2), advanced stage, treatment with chemotherapy, and sociodemographic factors including sex, age, race, household income, facility type (academic/community), and location (urban/rural). With a median follow-up time of 14.8 years (95% CI 14.6-not reached), median OS was lower in uninsured patients [13.4 years (12.3-not reached) vs 14.8 years (14.7-not reached); p < 0.0001]. Despite the lack of major changes in DLBCL therapies, a diagnosis after the implementation of the ACA (in 2010 or later) was associated with a superior OS when compared with the outcomes of patients diagnosed in 2010 or earlier [HR 0.93 (95% CI 0.90-0.95)]. Similarly, five-year OS was superior in the insured group [HR 0.93 (95% CI 0.89-0.96)]. Conclusions: Uninsured patients with DLBCL and < 64 years old had inferior OS when compared with insured patients, and uninsured status emerged as an independent risk factor for inferior OS. Our data highlight the independent effect of insurance disparities - a potential indicator of variations in access to health care - on survival in DLBCL.

1503 Background: Patients with cancer and COVID-19 are at risk for poor clinical outcomes. An established multi-site remote patient monitoring (RPM) service was rapidly adapted to support a novel, interdisciplinary COVID-19 program for outpatient management of patients at high-risk for severe illness. The goal of this study was to assess the impact of the RPM program on clinical outcomes and acute care utilization in cancer patients diagnosed with COVID-19. Methods: This is a cross-sectional analysis following a multi-site prospective observational study performed at Mayo Clinic Cancer Center (MCCC). All adult patients with active cancer – defined as currently receiving cancer-directed therapy or in recent remission on active surveillance – and PCR-confirmed SARS-CoV-2 infection between March 18 and July 31, 2020 were included. RPM was comprised of in-home technology to assess symptoms and physiologic data with centralized nurse and physician oversight. Results: During the study timeframe 224 cancer patients were diagnosed with COVID-19 at MCCC. Initial management included urgent hospitalization (within 48 hours of diagnosis) in 34 patients (15%). Of the remaining 190 patients (85%) initially managed in the outpatient setting, those who did not receive RPM were significantly more likely to experience hospitalization than those receiving RPM (OR 3.6, 95% CI 1.036 to 12.01, P = 0.044). Following balancing of patient characteristics by inverse propensity weighting, rates of hospital admission for RPM and non-RPM patients were 3.1% and 11% respectively, implying that RPM was associated with an 8% reduction in hospital admission rate (-0.077; 95% CI: -0.315 to -0.019, P = 0.009). Use of RPM was also associated with lower rates of prolonged hospitalization, ICU admission, and mortality, though these trends did not reach statistical significance. Conclusions: In the midst of a global pandemic associated with inpatient bed, ventilator, and PPE shortages, the RPM program provided an effective strategy for outpatient clinical management and was associated with decreased rates of hospitalization, ICU admission, and mortality in cancer patients with COVID-19. This care model enabled simultaneous opportunity to mitigate the increased risks of exposure, transmission, and resource utilization associated with conventional care.