Background: Patients (pts) with hematologic malignancies (HM) are thought to have suboptimal hospice utilization and receive more aggressive end of life (EOL) care than those with solid tumors (ST), including high rates of EOL chemotherapy use, ICU admissions, and inpatient deaths (Egan, Blood Adv, 2020). Barriers to equitable EOL care outcomes for pts with HM may include difficulty identifying the "terminal phase" of disease, the potential for curative stem cell transplant that lingers even after failure of multiple therapies (Odejide, JCO, 2016), and specificity of palliative needs in HM, like transfusion dependence (Leblanc, Blood, 2018), that are not met by the current hospice benefit. However, direct comparison of EOL outcomes between HM and ST in a population-based setting has not been conducted. Our objective was to compare EOL care quality measures, hospice utilization, and indicators of palliative needs between Medicare beneficiaries with HM and ST. Methods: From the linked SEER Medicare database (covering ~30% of the US population), we identified beneficiaries with common ST (breast, prostate, lung, and colorectal cancer) and HM (leukemia, myeloma, lymphoma, myelodysplastic syndrome [MDS], and myeloproliferative neoplasms [MPN]) who died between 2011-2015, and whose cause of death was cancer. We ascertained claims-based indicators of EOL care quality: hospice use before death, duration of hospice length of stay (LOS), death in an acute care hospital, receipt of (oral or parenteral) chemotherapy in the last 14 days of life (DOL), ICU admission in the last 30 DOL, Medicare spending, and inpatient days in the last 30 DOL. We also explored indicators of palliative needs: opioid use, transfusion use, and number of physician office visits in the last 30 DOL. We compared binary outcomes in multivariable robust Poisson (reporting adjusted relative risk, adj RR), counts in negative binomial, and costs in log-gamma models, reporting estimates with 95% confidence intervals (CI). Models were adjusted for age, sex, race, marital status, Medicaid co-insurance (indicator of low socio-economic status), prevalent poverty, comorbidity index, performance status indicator, calendar year, and survival from diagnosis. Results: Characteristics of the 18,185 patients with HM and 59,352 with ST are listed in Table. HM pts were, on average, older, and more likely to be male and married. HM pts were less likely than ST pts to enroll on hospice (58% vs 67%, adjusted RR 0.85,CI 0.84-0.86), had a shorter hospice LOS (median 9 vs 14 days, adj means ratio RR 0.81, CI 0.79-0.83), and were more likely to spend < 3 days on hospice (adj RR 1.29, CI 1.24-1.35) (see Figure). HM pts were more likely to die in an acute care setting (32% vs 23%, adj RR 1.4, CI 1.36-1.44), have an ICU admission in the last 30 DOL (39% vs 30%, adj RR 1.32, CI 1.29-1.35), and receive chemotherapy in the last 14 DOL (12% vs 5%, adj RR 2.73, CI 2.55-2.93). Median Medicare spending in the last 30 DOL was higher in HM than in ST (17.8k vs 11.9k, adj means ratio 1.52, CI 1.49-1.56), as was the median number of inpatient days (4 vs 2, adj means ratio 1.55, CI 1.52-1.59). The results were consistent when examined by specific subtypes of HM and ST. Pts with HM were less likely to use opioids at the EOL (adj RR 0.81, 95% CI, 0.79-0.84), but had more transfusions (adj RR 4.34, 95% CI, 4.11-4.58) and more physician office visits (adj RR 1.11, 95% CI, 1.09-1.14). Furthermore, trends in EOL care quality indicators differed between HM and ST. While the use of hospice services increased for both populations, the hospice LOS has steadily lengthened over time in ST (from median 10 days in 2011 to 14 days in 2015, P<.001), but it did not change significantly in HM (P=.077). Discussion: To our knowledge, this is the first population-based study demonstrating that, adjusting for socio-demographic characteristics and baseline health status, pts with HM have inferior EOL care quality outcomes than those with ST. These disparities are consistent across all established EOL care quality outcomes and across all histologies, supporting the notion of a fundamental difference between EOL care in HM and ST. Furthermore, our data challenge the assumption that HM pts do not have significant palliative care needs; rather, their needs may differ from those of ST patients, and may be less easily met by the current hospice benefit (as other literature suggests). Novel care models are needed to improve EOL care for pts with HM. Disclosures Panagiotou: International Consulting Associates, Inc: Other: personal fees from International Consulting Associates, Inc. outside the scope of the submitted work. LeBlanc:AstraZeneca: Research Funding; Agios, AbbVie, and Bristol Myers Squibb/Celgene: Speakers Bureau; UpToDate: Patents & Royalties; American Cancer Society, BMS, Duke University, NINR/NIH, Jazz Pharmaceuticals, Seattle Genetics: Research Funding; AbbVie, Agios, Amgen, AstraZeneca, CareVive, BMS/Celgene, Daiichi-Sankyo, Flatiron, Helsinn, Heron, Otsuka, Medtronic, Pfizer, Seattle Genetics, Welvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Olszewski:Adaptive Biotechnologies: Research Funding; Spectrum Pharmaceuticals: Research Funding; Genentech, Inc.: Research Funding; TG Therapeutics: Research Funding.
Introduction: While an association between AI diseases and the development of lymphoma (LYM) is known, the impact of AI diseases on patient (pt) outcome across varying LYM subtypes, including the role of immunosuppressive medications (ISM), is not well understood. Methods: We conducted a large, multicenter, RWE retrospective analysis of adult pts with a diagnosis (dx) of lymphoma (LYM) between 1/2000 and 12/2020 and a pre-existing AI disease (see full list AI in Table 1). We examined baseline clinical features at LYM diagnosis, underlying AI disease characteristics, type/duration of ISM, LYM therapy received and survival outcomes. Multinomial regression models adjusted for age and sex identified associations between LYM subtypes, AI diseases, and ISM exposure, and were reported as relative risk ratio (RRR). Survival rates were estimated by Kaplan-Meier. Univariate associations of baseline factors with survival in cases with non-missing data were examined by Cox model and stratified by International Prognostic Index (IPI). Results: In total, 785 pts were identified across 14 North American institutions, of which 694 pts with complete data were included in the final analysis. Rheumatoid arthritis was the most common AI disease (30%) (Table 1). Diffuse large B-cell lymphoma (DLBCL) was the most common histologic subtype (n=303, 44%), and it was the most prevalent histology for nearly every AI disease. The median duration between AI disease and LYM diagnosis was 108 months (mo) (1-816 mo). Several associations were found between specific AI diseases and LYM subtypes, including: Hashimoto's thyroiditis with marginal zone lymphoma (MZL, RRR 2.78 (95% CI 1.14-6.78, P=0.024), Waldenstrom's macroglobulinemia (RRR 7.12 (95% CI 1.07-47.3, P=0.031), and follicular lymphoma (RRR, 2.71, 95% CI 1.01-7.24, P=0.047); Polymyalgia rheumatica with CLL/SLL (RRR 21.27 (95% CI 4.57-98.96), P=0.0001) and MZL (RRR 6.62 (95% CI 1.38-31.80), P=0.018); Psoriasis with peripheral T-cell lymphoma (PTCL, RRR 3.85 (95% CI 1.21-12.22), P=0.022); and Inflammatory bowel disease with CLL/SLL (HR 4.60 (95% CI 1.72-12.34), P=0.002). Overall, 402 (58%) pts had ISM exposure prior to LYM dx, with 279 (40%) pts on active ISM at time of LYM dx. The most commonly used ISM agent was methotrexate (26%); the median duration of ISM prior to LYM diagnosis was 60 mo (1-480). Compared with DLBCL pts, those diagnosed with PTCLs were more likely to have had exposure to TNF-α inhibitors (RR 1.89, 95% CI 1.23-2.89, P=0.004) or apremilast, a phosphodiesterase-4 inhibitor (RR 19.2, 95% CI 2.6-149.9, P=0.005). Rituximab +/- chemotherapy was used as frontline therapy in the majority of B-cell LYM pts, including 90% of DLBCL. Of LYM pts on ISM at initial dx, 41% had a reduction in immunosuppression (RIS), of whom 81% stopped ISM completely (Table 1). Only 10% of pts who underwent RIS were reported to have a flare of AI disease during initial therapy, compared with 7% of pts who had a flare despite no RIS (P=NS). Survival by histology is depicted in Figure A/B. For DLBCL pts (n=303), survival appeared overall comparable to historically reported outcomes in the general DLBCL population. Neither antecedent use of ISM nor duration of ISM were associated with pt outcomes (data not shown). However, a survival advantage was identified among DLBCL pts whose ISM was stopped completely during frontline therapy compared with pts who were not on ISM at diagnosis (Figure C/D). In addition, 10 DLBCL pts underwent 100% RIS +/- rituximab with therapeutic intent and did not receive cytotoxic chemotherapy as part of frontline therapy. Among these, 4 pts (mean age 62 years (26-84); ISM: n=2 MTX, and 1 each azathioprine and etanercept/MTX; EBV+ 2/4) achieved complete remission and remain disease-free at time of last follow-up at 5, 74, 105, and 221 months post-DLBCL dx. Conclusions: Altogether, we identified several novel histologic associations of AI diseases with LYM histologic subtypes. Furthermore, prior receipt of TNF-α inhibitor or a phosphoediesterase 4 inhibitor were associated with a diagnosis of PTCL. For DLBCL pts with antecedent AI disease, complete cessation of ISM during frontline LYM therapy appeared to be associated with improved survival. Finally, there were a small number of select DLBCL pts who garnered long-term disease-free survival using RIS +/- rituximab as frontline therapy, similar to the treatment paradigm in post-transplant lymphoproliferative disorders. Figure 1 Figure 1. Disclosures Olszewski: TG Therapeutics: Research Funding; PrecisionBio: Research Funding; Celldex Therapeutics: Research Funding; Acrotech Pharma: Research Funding; Genentech, Inc.: Research Funding; Genmab: Research Funding. Feldman: Alexion, AstraZeneca Rare Disease: Honoraria, Other: Study investigator. Karmali: Karyopharm: Consultancy; Epizyme: Consultancy; Morphosys: Consultancy, Speakers Bureau; Kite, a Gilead Company: Consultancy, Research Funding, Speakers Bureau; Genentech: Consultancy; AstraZeneca: Speakers Bureau; BeiGene: Consultancy, Speakers Bureau; BMS/Celgene/Juno: Consultancy, Research Funding; EUSA: Consultancy; Janssen/Pharmacyclics: Consultancy; Roche: Consultancy; Takeda: Research Funding. Lunning: TG Therapeutics: Consultancy; Janssen: Consultancy; Verastem: Consultancy; Spectrum: Consultancy; Myeloid Therapeutics: Consultancy; AstraZeneca: Consultancy; Morphosys: Consultancy; ADC Therapeutics: Consultancy; Beigene: Consultancy; Daiichi-Sankyo: Consultancy; Kyowa Kirin: Consultancy; Celgene, a Bristol Myers Squibb Co.: Consultancy; Legend: Consultancy; Acrotech: Consultancy; Novartis: Consultancy; Kite, a Gilead Company: Consultancy; Karyopharm: Consultancy; AbbVie: Consultancy. Palmisiano: Genentech: Research Funding; AbbVie: Consultancy, Research Funding; Takeda: Consultancy; Foundation One: Consultancy. Danilov: Bristol-Meyers-Squibb: Honoraria, Research Funding; Gilead Sciences: Research Funding; Pharmacyclics: Consultancy, Honoraria; Beigene: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; TG Therapeutics: Consultancy, Research Funding; Takeda Oncology: Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Bayer Oncology: Consultancy, Honoraria, Research Funding; Astra Zeneca: Consultancy, Honoraria, Research Funding; SecuraBio: Research Funding; Rigel Pharm: Honoraria. Barta: Kyowa Kirin: Honoraria; Acrotech: Honoraria; Daiichi Sankyo: Honoraria; Seagen: Honoraria. Lansigan: Celgene/BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees. Savage: Astra-Zeneca: Consultancy, Honoraria; Takeda: Other: Institutional clinical trial funding; Roche: Research Funding; Merck: Consultancy, Honoraria, Other: Institutional clinical trial funding; BMS: Consultancy, Honoraria, Other: Institutional clinical trial funding; Seattle Genetics: Consultancy, Honoraria; Servier: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Beigene: Other: Institutional clinical trial funding; Genentech: Research Funding.
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