Citations

Abstract Objective . We proposed an experimental approach to build a precise machine-specific beam delivery time (BDT) prediction and delivery sequence model for standard, volumetric, and layer repainting delivery based on a cyclotron accelerator system. Approach . Test fields and clinical treatment plans’ log files were used to experimentally derive three main beam delivery parameters that impacted BDT: energy layer switching time (ELST), spot switching time, and spot drill time. This derived machine-specific model includes standard, volumetric, and layer repainting delivery sequences. A total of 103 clinical treatment fields were used to validate the model. Main results . The study found that ELST is not stochastic in this specific machine. Instead, it is actually the data transmission time or energy selection time, whichever takes longer. The validation showed that the accuracy of each component of the BDT matches well between machine log files and the model’s prediction. The average total BDT was about (−0.74 ± 3.33)% difference compared to the actual treatment log files, which is improved from the current commercial proton therapy system’s prediction (67.22%±26.19%). Significance . An accurate BDT prediction and delivery sequence model was established for an cyclotron-based proton therapy system IBA ProteusPLUS ® . Most institutions could adopt this method to build a machine-specific model for their own proton system.

Abstract Despite numerous therapeutic advances in pulmonary arterial hypertension, patients continue to suffer high morbidity and mortality, particularly considering a median age of 50 years. This article explores whether early, robust reduction of right ventricular afterload would facilitate substantial improvement in right ventricular function and thus whether afterload reduction should be a treatment goal for pulmonary arterial hypertension. The earliest clinical studies of prostanoid treatment in pulmonary arterial hypertension demonstrated an important link between lowering mean pulmonary arterial pressure (or pulmonary vascular resistance) and improved survival. Subsequent studies of oral monotherapy or sequential combination therapy demonstrated smaller reductions in mean pulmonary arterial pressure and pulmonary vascular resistance. More recently, retrospective reports of initial aggressive prostanoid treatment or initial combination oral and parenteral therapy have shown marked afterload reduction along with significant improvements in right ventricular function. Some data suggest that reaching threshold levels for pressure or resistance (components of right ventricular afterload) may be key to interrupting the self-perpetuating injury of pulmonary vascular disease in pulmonary arterial hypertension and could translate into improved long-term clinical outcomes. Based on these clues, the authors postulate that improved clinical outcomes might be achieved by targeting significant afterload reduction with initial oral combination therapy and early parenteral prostanoids.

Background & Purpose It has been hypothesized that circulating neutrophils have a direct correlation with the composition of emboli in acute ischemic stroke (AIS). The aim of this study is to evaluate the association between neutrophil-lymphocyte ratio (NLR) in peripheral blood and the expression of neutrophil extracellular traps (NETs) within stroke emboli. Methods Consecutive patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) that underwent mechanical thrombectomy (MT) were included. Patients were divided into two groups based on NLR median value. Retrieved thrombi were histologically analyzed using Martius Scarlett Blue (MSB) for main thrombus components including red blood cells (RBCs), white blood cells (WBCs), fibrin and platelet. Immunohistochemistry staining for von Willebrand Factor (vWF) and anti-citrullinated H3 (H3Cit; NETs marker) was also performed. Results Samples from a total of 84 patients were included. The average percentage of RBCs, WBCs, fibrin, platelet, H3Cit, and vWF components in thrombi were 45.1%, 3.5%, 21.8%, 29.6%, 19.7% and 14.8% respectively. When stratifying by NLR group [low (≤3.94) versus high (>3.95)], high NLR group had significantly more WBCs (4.5%), fibrin (24.2%), H3Cit (22.7%) and vWF (17.1%) thrombus fractions compared to low NLR group. Additionally, RBC content (38.8%) was lower in the high NLR group. Conclusions NLR is correlated with the amounts of WBCs, fibrin, NETs and vWF within the thrombi retrieved from AIS patients due to LVO.

Abstract Introduction: Selinexor is a first-in-class, central nervous system (CNS) penetrant, oral inhibitor of exportin 1 (XPO1), the sole nuclear exporter of many key tumor suppressors. Selinexor is FDA-approved for refractory multiple myeloma and DLBCL and has been evaluated in a phase 1 trial in children with leukemia. We report a phase 1 trial of selinexor in children and adolescents with recurrent CNS and solid tumors, including lymphoma (NCT02323880). Methods: A rolling-six design was used to evaluate selinexor (10 or 25 mg tablets) administered twice or once weekly during a 28-day cycle. Dose determination was based on protocol-defined dose limiting toxicity (DLT) using CTCAEv4 during cycle 1. First dose pharmacokinetics (PK) were performed. Results: 43 subjects were enrolled (17 males); median (range) age was 15 (6-20) years. 27 (63%) had CNS tumors, most commonly high-grade glioma (n=12); 16 (37%) had extracranial solid tumors. 42 were evaluable for DLT. At the starting dose (35 mg/m2/dose, twice weekly), no DLTs were observed in 6 subjects, however, 2 subjects had unexpected late myelosuppression delaying initiation of cycle 2. The dosing schedule was amended to twice weekly for 3 weeks followed by a 1 week break. 12 subjects received 35 mg/m2/dose; 4 experienced DLTs [grade 3 fatigue (n=2), grade 3 thrombocytopenia (n=1), or grade 3 ALT increase (n=1)]. The dose was de-escalated to 20 mg/m2/dose, 3 weeks on, 1 week off. 12 subjects enrolled; 3 experienced a DLT [grade 3 increased AST/ALT, acute reversible neurologic changes, or neutropenia (each n=1)]. At the 20 mg/m2 (n=12) and 35 mg/m2 (n=19) dose levels, respectively, the mean ± SD Cmax (ng/ml) was 324±116 and 535±174, and AUC (hr•ng/ml) was 3092 ± 842 and 5156 ± 1227. This was comparable to PK in adults receiving 35 and 50 mg/m2. Based on a desire to achieve a higher Cmax and avoid breaks in schedule, and emerging evidence for similar effectiveness with decreased toxicity in adults receiving continuous once weekly dosing, we evaluated a dosing schedule with once weekly dosing for all 4 weeks of each cycle. At the initial dose level (45 mg/m2 weekly), 2 of 6 subjects had DLTs [prolonged grade 2 thrombocytopenia or grade 3 seizure in a primary CNS tumor patient]. Six subjects received 35 mg/m2/dose once weekly; 1 DLT [grade 3 thrombocytopenia] was observed. Non-dose-limiting toxicity (Grade ≥2 occurring in >10% of subjects during cycle 1) included lymphopenia, leukopenia, neutropenia, thrombocytopenia, anorexia, fatigue, hypophosphatemia, nausea, and vomiting. Subjects received a median (range) of 1 (1-9) cycle; 13 received 2-3 cycles, and 6 received 5-9 cycles. Conclusions: Selinexor-related toxicities were primarily hematological and gastrointestinal. The maximum tolerated dose (MTD) of selinexor in children and adolescents with recurrent solid and CNS tumors is 20 mg/m2/dose twice weekly for 3 weeks followed by one week off. On a continuous once weekly schedule, the MTD and recommended phase 2 starting dose of selinexor is 35 mg/m2/dose. Citation Format: Adam L. Green, Charles G. Minard, Xiaowei Liu, Joel M. Reid, Kerice Pinkney, Stephan Voss, Marvin D. Nelson, Elizabeth Fox, Brenda J. Weigel, Julia Glade Bender. Phase 1 trial of selinexor in children and adolescents with recurrent/refractory solid and CNS tumors (ADVL1414): A Children’s Oncology Group Phase 1 Consortium trial [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P162.

AbstractPurposeDue to the employment of quadratic programming using soft constraints to implement dose volume constraints and the “trial‐and‐error” procedure needed to achieve a clinically acceptable plan, conventional dose volume constraints (upper limit) are not adequately effective in controlling small and isolated hot spots in the dose/linear energy transfer (LET) distribution. Such hot spots can lead to adverse events. In order to mitigate the risk of brain necrosis, one of the most clinically significant adverse events in patients receiving intensity‐modulated proton therapy (IMPT) for base of skull (BOS) cancer, we propose per‐voxel constraints to minimize hot spots in LET‐guided robust optimization.Methods and materialsTen BOS cancer patients treated with IMPT were carefully selected by meeting one of the following conditions: (1) diagnosis of brain necrosis during follow‐up; and (2) considered high risk for brain necrosis by not meeting dose constraints to the brain. An optimizing structure (BrainOPT) and an evaluating structure (BrainROI) that both contained the aforementioned hot dose regions in the brain were generated for optimization and evaluation, respectively. Two plans were generated for every patient: one using conventional dose‐only robust optimization, the other using LET‐guided robust optimization. The impact of LET was integrated into the optimization via a term of extra biological dose (xBD). A novel optimization tool of per‐voxel constraints to control small and isolated hot spots in either the dose, LET, or combined (dose/LET) distribution was developed and used to minimize dose/LET hot spots of the selected structures. Indices from dose‐volume histogram (DVH) and xBD dose‐volume histogram (xBDVH) were used in the plan evaluation. A newly developed tool of the dose‐LET‐volume histogram (DLVH) was also adopted to illustrate the underlying mechanism. Wilcoxon signed‐rank test was used for statistical comparison of the DVH and xBDVH indices between the conventional dose‐only and the LET‐guided robustly optimized plans.ResultsPer‐voxel constraints effectively and efficiently minimized dose hot spots in both dose‐only and LET‐guided robust optimization and LET hot spots in LET‐guided robust optimization. Compared to the conventional dose‐only robust optimization, the LET‐guided robust optimization could generate plans with statistically lower xBD hot spots in BrainROI (VxBD,50 Gy[RBE], p = 0.009; VxBD,60 Gy[RBE], p = 0.025; xBD1cc, p = 0.017; xBD2cc, p = 0.022) with comparable dose coverage, dose hot spots in the target, and dose hot spots in BrainROI. DLVH analysis indicated that LET‐guided robust optimization could either reduce LET at the same dose level or redistribute high LET from high dose regions to low dose regions.ConclusionPer‐voxel constraint is a powerful tool to minimize dose/LET hot spots in IMPT. The LET‐guided robustly optimized plans outperformed the conventional dose‐only robustly optimized plans in terms of xBD hot spots control.

Background. Long noncoding RNAs (lncRNAs) represent an important novel class of noncoding RNA molecule greater than 200 nucleotides that play a key role in the regulation of autoimmune diseases. Previous studies have demonstrated that MAFTRR (MAF transcriptional regulator RNA) regulated Th1 cells differentiation by inhibiting the expression of MAF in activated CD4+ T cells. However, the effect of MAFTRR on the pathogenesis of Hashimoto’s thyroiditis (HT) remains unclear. This research was aimed at investigating the expression of MAFTRR in Hashimoto’s thyroiditis (HT) as well as the correlation between MAFTRR and Th1 cells. Methods. Thirty-eight HT patients and thirty-eight healthy controls were enrolled in the study. The proportion of Th1 cells and CD8+IFN-γ+ T cells in peripheral blood mononuclear cells (PBMCs) from these specimens was determined by flow cytometric analysis. The transcript levels of MAFTRR, MAF, and IFNG in PBMCs and thyroid glands were detected by quantitative real-time PCR. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the potential value of MAFTRR in the HT patients. Results. We found that the proportion of circulating Th1 cells and the transcript levels of IFNG were increased in peripheral blood of the HT patients. The transcript levels of MAFTRR were significantly increased in the HT patients and positively correlated with the percentage of Th1 cells and serum levels of antithyroglobulin antibody and antithyroperoxidase antibody. The transcript levels of MAF, a transcription factor that inhibits Th1 cells activity and IFN-γ production, were attenuated in PBMCs from the HT patients. The transcript levels of IFNG had positive and inverse correlations with MAFTRR and MAF expression in PBMCs from the HT patients, respectively. Additionally, a significantly positive correlation between upregulated MAFTRR expression and augmented IFNG expression was revealed in thyroid tissues from the HT patients. ROC curve suggested that MAFTRR could potentially differentiate the HT patients from healthy controls. Conclusion. MAFTRR is significantly augmented in the HT patients and may contribute to the pathogenic role of the Th1 cells response in HT.

Abstract Introduction: Treatment resistance remains a challenge that most patients (pts) with MM will encounter in their disease course. With each subsequent line of therapy (LOT), pt outcomes and health-related quality of life (HRQoL) worsen. Pts with MM who are refractory to immunomodulatory drugs, proteasome inhibitors (PIs), and anti-CD38 antibodies (triple-class refractory; TCR) have a poor prognosis and the holistic burden is not well understood. To capture these features, the ongoing prospective Connect ® MM Disease Registry was used to report treatment patterns and outcomes in pts with TCR MM. Methods: Data from September 28, 2009 to February 4, 2021 were used. Eligible pts were ≥18 years of age at first documented diagnosis of MM, which became TCR during their disease course. MM was defined as refractory to therapies based on the International Myeloma Working Group Criteria. Index date was defined as first record of TCR during the disease course. Data analyzed included pt characteristics, treatment patterns pre- and post-index, overall survival (OS), healthcare resource utilization (HCRU), and HRQoL. General HRQoL was captured by EQ-5D-3L utility index (range −0.11-1, higher score indicated better HRQoL), EQ-5D visual analog scale (VAS) score (range 0-100, "worst imaginable" to "best imaginable" health status), and FACT-G total score (range 0-108). Disease-specific HRQoL was measured by FACT-MM total score (range 0-164), FACT-MM Trial Outcome Index (TOI) (range 0-112), and the Brief Pain Inventory (BPI) average pain item (range 0-10, "no pain" to "worst pain you can imagine"). Higher scores indicated better HRQoL for all FACT-related index scores. Cohort-level clinically meaningful change in HRQoL during post-index year 1 was defined as 0.08 for the EQ-5D utility index, 7 for the EQ-5D VAS, and 6-8 points for the FACT-G total score. Validated thresholds were unavailable for other index scores. All statistical analyses were descriptive. Results: This analysis included 240 pts identified from the Registry. Mean age at index date was 68.1 (standard deviation [SD] 10.4) years. Mean time from initial MM diagnosis was 64.1 (SD 26.9) months. 63.3% of pts had prior stem cell transplantation. Mean number of prior LOTs was 4 (SD 1.7). 64.6% of pts had received a subsequent MM LOT since index date. Median follow-up time post-index date was 4.6 months. Treatment pattern analysis of post-index treated pts (Table A) revealed that retreatment with an immunomodulatory drug, PI, or anti-CD38 agent post-index date was common, led by carfilzomib (47.1%) and pomalidomide (40.0%). However, no therapeutic combination was preferred in this pt population. Regarding their index therapy (first post-index LOT), 70.3% and 22.6% were on triplet and quadruplet combination therapy; 3.2% initiated belantamab mafodotin, selinexor, melflufen, and/or idecabtagene vicleucel; mean duration of treatment was 5.6 (SD 6.1) months. For all pts, median OS was 8.9 months; median OS for those who received < 4 prior LOTs (43.8%) versus those who received 4 or more prior LOTs (56.2%) was 10.0 and 8.0 months, respectively. Regarding HCRU during the post-index follow-up, all-cause and MM-related hospitalizations were observed in 49.6% and 23.8% of all eligible pts, respectively. Among these pts, annualized mean number of all-cause/MM-related hospitalization and length of hospital stay were estimated as 8.5 (SD 18.2)/11.0 (SD 25.2) and 64.0 days (SD 80.0)/ 69.2 days (SD 83.7), respectively. Analysis of HRQoL showed a numerical decline in HRQoL relative to index during the 1-year post-index period across all studied HRQoL instruments. There was a clinically meaningful decline in HRQoL from baseline following the index date based on the EQ-5D utility index, EQ-5D VAS, and FACT-G total score (Table B). Conclusion: Pts with TCR MM experienced poor survival, substantial hospitalizations, and a clinically meaningful decline in HRQoL. 35.4% of pts did not receive any new LOTs since the disease became TCR. Among those who did receive subsequent LOTs, re-treatment with an immunomodulatory drug, PI, or anti-CD38 antibody therapy occurred, showing a lack of novel treatment options. This study suggests novel tolerable and efficacious therapeutic agents are needed to address the burden of illness in pts with MM. Figure 1 Figure 1. Disclosures Tang: Bristol-Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Hari: Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Celgene-BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Adaptive Biotech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Karyopharm: Consultancy; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Millenium: Membership on an entity's Board of Directors or advisory committees, Research Funding. Ramasamy: Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene - Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Weisel: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy; Novartis: Honoraria; Pfizer: Honoraria. Joshi: Bristol Myers Squibb: Current Employment. Liu: Bristol Myers Squibb: Current Employment. Che: Bristol Myers Squibb: Current Employment. Hernandez: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Abonour: Jensen: Honoraria, Research Funding; Celgene-BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Honoraria, Research Funding; Takeda: Research Funding. Hardin: Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees. Rifkin: Sanofi: Membership on an entity's Board of Directors or advisory committees; McKesson: Current Employment, Current equity holder in publicly-traded company; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Fresenius-Kabi: Membership on an entity's Board of Directors or advisory committees; Coherus: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb (Celgene): Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Ailawadhi: BeiGene, Ltd.: Consultancy; Bristol Myers Squibb: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Ascentage: Research Funding; Medimmune: Research Funding; Cellectar: Research Funding; Xencor: Research Funding; GSK: Consultancy, Research Funding; Takeda: Consultancy; Genentech: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy; Sanofi: Consultancy; Oncopeptides: Consultancy. Lee: Legend Biotech: Consultancy; Sanofi: Consultancy; Oncopetides: Consultancy; Bristol Myers Squibb: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Consultancy; Janssen: Consultancy, Research Funding; Takeda Pharmaceuticals: Consultancy, Research Funding; Karyopharm: Consultancy; Regeneron: Research Funding; Genentech: Consultancy; GlaxoSmithKline: Consultancy, Research Funding. Terebelo: Pharmacylics: Speakers Bureau; Celgene-BMS: Consultancy; Takeda: Speakers Bureau; Janssen: Speakers Bureau. Durie: Amgen, Celgene/Bristol-Myers Squibb, Janssen, and Takeda: Consultancy; Amgen: Other: fees from non-CME/CE services . Narang: Bristol Myers Squibb: Consultancy; TG Pharma: Consultancy. Toomey: Bristol Myers Squibb: Consultancy. Gasparetto: Oncopeptide: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy; Sanofi: Consultancy, Honoraria, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Speakers Bureau; Gsk: Consultancy, Honoraria, Speakers Bureau. Wagner: Celgene-BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Athenex Inc: Consultancy; Eli Lilly, Johnson & Johnson: Other: Spouse, previously held individual stocks;. Jagannath: Janssen Pharmaceuticals: Consultancy; Bristol Myers Squibb: Consultancy; Karyopharm Therapeutics: Consultancy; Legend Biotech: Consultancy; Sanofi: Consultancy; Takeda: Consultancy.

Abstract Introduction: Mantle cell lymphoma (MCL), Waldenström macroglobulinemia (WM), marginal zone lymphoma (MZL) and chronic lymphocytic leukemia (CLL) are subtypes of non-Hodgkin's lymphoma. Collectively, these subtypes constitute a large proportion of all B-cell malignancies; however, they are typically associated with survival spanning several years with multiple interspersed treatment periods due to frequent relapses. This can predispose patients to repeated hospitalization resulting in significant economic impact. The objectives of this study were to examine real-world treatment patterns, costs and healthcare resource utilization for patients with these lymphomas, as well as identify disparities and risk factors associated with costs incurred in US hospitals. Methods: A retrospective study was conducted using the Premier Healthcare Database, a geographically diverse all-payer, hospital administrative database containing more than one billion inpatient and hospital-based outpatient encounters. Patients aged ≥18 years with at least 1 inpatient or 2 hospital-based outpatient visits with a MCL, WM, CLL, or MZL diagnosis, and who received treatment for these conditions from 1/1/2014 to 10/31/2019 were included in the study. Descriptive analysis was conducted to examine patient sociodemographic and hospital characteristics, all-cause and lymphoma-related healthcare resource utilization and costs. Costs were obtained from patients' discharge files and hospital billing records. Multivariable analysis was performed using generalized linear models to examine risk factors associated with costs and LOS for each lymphoma type. Treatment evaluated including any use of supportive care (blood transfusion, granulocyte colony stimulating factors [GCSF]), and by regimen (steroids alone, chemotherapy alone, chemo-immunotherapy, rituximab alone, targeted therapy alone, targeted therapy in combination with chemotherapy, other). Statistical significance was determined as a p-value of <0.05. Results: The study population included 23,952 CLL, 3,387 MCL, 2,655 MZL, and 1,811 WM patients in the US hospital database. Patients with WM were statistically significantly older (77 ± 58.5 years) compared to patients with CLL (74.2 ± 30.2 years), MCL (70.7 ± 37.3 years) or MZL (69.3 ± 33.8 years). More male (71.7%) and white (87.8%) patients were identified in the MCL cohort compared to the general lymphoma cohorts (Males: 58.7%; whites: 86.3). The most common comorbidities included chronic pulmonary disease (27.1%), gastroesophageal reflux disease (17.6%), moderate-severe renal disease (16.6%), congestive heart failure (15.7%) and diabetes without chronic complications (15.2%). Overall, more than two-thirds of patients in each lymphoma group received treatment with steroids alone during hospitalizations. While the use of steroids alone was higher among whites compared to non-white patients (69.5% vs. 60.4%), the use of chemo-immunotherapy was lower in whites compared to non-whites (11.9% vs. 16.2%). The average LOS of inpatient hospitalization ranged from 6.3 days for CLL to 7.4 days for MCL, with the mean costs per hospitalization from $19,566 (CLL) to $24,439 (MCL). Non-white patients have significantly longer mean LOS days compared with white patients (CLL: 18.3 vs. 14.8; MCL: 21.7 vs. 18.3; MZL: 21.6 vs. 18.5; WM 19.0 vs. 14.5). Across the 4 lymphoma types, multivariable regression confirmed the descriptive results and demonstrated that higher hospitals costs were associated with patients who were non-white, Hispanic/Latino, treated in hospitals located in the Northeast or West, or had Medicaid; statistically significant increased cost of care was also noted for patients who received targeted therapy or supportive care, such as blood transfusion or GCSF (Table 1). Conclusions: Real-world data demonstrated the significantly high total hospital costs once patients with MCL, WM, MZL, and CLL patients were hospitalized, with significantly higher impact to minority populations. Given the increased availability of effective oral therapeutics, optimal and timely disease control in the outpatients setting can potentially prevent or decrease hospitalizations and reduce economic burden on healthcare. Future studies are needed to explore the reason for admission, clinical outcomes, and potential preventive interventions. Figure 1 Figure 1. Disclosures Chanan-Khan: Alpha2 Pharmaceuticals: Patents & Royalties: Tabi; Ascentage, Starton, Cellectar, NonoDev, Alpha2 Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; BeiGene, Jansen, Ascentage: Honoraria; Cellectar: Current equity holder in publicly-traded company; Ascentage: Research Funding; Alpha2 Pharmaceuticals, NonoDev, Starton: Current holder of stock options in a privately-held company; BieGene, Jansen, Ascentage: Consultancy. Yang: BeiGene, Ltd.: Current Employment. Liu: BeiGene, Ltd.: Current Employment. Cao: Premier, Inc.: Current Employment; BeiGene, Ltd.: Consultancy. Baumer: BeiGene, Ltd.: Consultancy; Premier, Inc.: Current Employment. Krishnaswami: BeiGene, Ltd.: Consultancy; Premier, Inc.: Current Employment. Tang: BeiGene, Ltd.: Current Employment. Ailawadhi: Karyopharm: Consultancy; AbbVie: Consultancy; Genentech: Consultancy; Takeda: Consultancy; GSK: Consultancy, Research Funding; Xencor: Research Funding; Cellectar: Research Funding; Medimmune: Research Funding; Ascentage: Research Funding; Pharmacyclics: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; BeiGene, Ltd.: Consultancy; Sanofi: Consultancy; Oncopeptides: Consultancy.