Inactive status is an independent predictor of liver transplant waitlist mortality and is associated with a transplant centers median meld at transplant

Background Approximately 30% of patients on the liver transplant waitlist experience at least one inactive status change which makes them temporarily ineligible to receive a deceased donor transplant. We hypothesized that inactive status would be associated with higher mortality which may differ on a transplant centers’ or donor service areas’ (DSA) Median MELD at Transplant (MMaT). Methods Multi-state models were constructed (OPTN database;06/18/2013-06/08/2018) using DSA-level and transplant center-level data where MMaT were numerically ranked and categorized into tertiles. Hazards ratios were calculated between DSA and transplant center tertiles, stratified by MELD score, to determine differences in inactive to active transition probabilities. Results 7,625 (30.2% of sample registrants;25,216 total) experienced at least one inactive status change in the DSA-level cohort and 7,623 experienced at least one inactive status change in the transplant-center level cohort (30.2% of sample registrants;25,211 total). Inactive patients with MELD≤34 had a higher probability of becoming re-activated if they were waitlisted in a low or medium MMaT transplant center or DSA. Transplant rates were higher and lower re-activation probability was associated with higher mortality for the MELD 26–34 group in the high MMaT tertile. There were no significant differences in re-activation, transplant probability, or waitlist mortality for inactivated patients with MELD≥35 regardless of a DSA’s or center’s MMaT. Conclusion This study shows that an inactive status change is independently associated with waitlist mortality. This association differs by a centers’ and a DSAs’ MMaT. Prioritization through care coordination to resolve issues of inactivity is fundamental to improving access.

Utilization of Stem Cell Transplantation in Newly Diagnosed Multiple Myeloma Patients Lacking Geographic Proximity to a Transplant Center

Introduction: Autologous stem cell transplant (ASCT) is considered standard of care in young and fit patients with newly diagnosed multiple myeloma. ASCT has shown to improve depth of response, progression free survival and overall survival compared to systemic therapy alone in myeloma patients (Harousseau et al. New England Journal of Medicine). Proximity to a stem cell transplant center may influence the utilization of this therapeutic option in transplant eligible multiple myeloma patients. Our cancer center did not have a stem cell transplant program in the 100-mile driving radius. The goal of this study was to assess the referral patterns and utilization of ASCT in newly diagnosed, young (age <65 years) multiple myeloma patients in a setting where patients are lacking proximity to a transplant center. Methods: The study was an IRB-approved retrospective cohort study. Patients between 18 and 65 years of age at the time of diagnosis who were diagnosed with multiple myeloma between January 1, 2014, and December 31, 2020, were included. Data including age at diagnosis, sex, race, zip code, treatment regimen, clinical data-including referral to a transplant center, stem cell collection and transplant-were collected and analyzed. Staging was calculated using lab values at the time of diagnosis or within 2 weeks of starting treatment. Date of diagnosis was defined as the date of bone marrow biopsy confirming systemic disease. All frequency and descriptive analyses were performed using SPSS Version 26 (Armonk, NY: IBM Corp.) Results: There were n = 62 patients that met the study inclusion criteria. Patients were mainly white (86%) and male (58%) with an average age at diagnosis of 55.9 (SD = 6.83) years. All patients (n = 62, 100%) lived at zip codes that were more than 100 miles from the closest transplant center. ISS staging showed 37% (n = 23, 95% CI 25% - 50%), 29% (n = 18, 95% CI 18% - 42%), and 18% (n = 11, 95% CI 9% - 30%) to have stage I, II, and III disease respectively. Twelve patients (n = 12, 19.4%, 95% CI 10.4% - 31.4%) had insufficient data for staging. The most common first line regimens were bortezomib, lenalidomide, and dexamethasone (n = 39, 62.9%, 95% CI 49.7% - 74.8%) and bortezomib, cyclophosphamide, and dexamethasone (n = 13, 21%, 95% CI 11.7% - 33.2%). Most patients (n = 48, 77.4%, 95% CI 65% - 87.1%) achieved a very good partial response or better. Eight (n = 8, 13%, 95% CI 5.7% - 23.9%) patients had refractory disease to first line therapy. Forty-six (n = 46, 74%, 95% CI 62% - 85%) patients were referred for HSCT evaluation, n = 16 (26%, 95% CI 15.5% - 38.5%) patients were not. Of the forty-six (n = 46) patients that were referred, n = 44 (96%, 95% CI 85% - 99.5%) patients had a clinical consultation with the transplant team. Of the entire cohort, n = 36 (58%, 95% CI 44.9% - 70.5%) patients underwent stem cell collection and n = 34 (55%, 95% CI 42% - 68%) patients underwent an ASCT after induction therapy. Conclusions: Our study found that more than one third of young patients with newly diagnosed multiple myeloma did not undergo stem cell collection or stem cell transplant. Lack of geographic access to a transplant center may be a contributing factor to the under utilization of this highly effective therapeutic strategy. Further investigation into interventions to improve ASCT referral and completion rates is imperative for improving outcomes for patients in such geographic locations. Disclosures Raj: Amgen: Membership on an entity's Board of Directors or advisory committees; Jazz pharmaceuticals: Speakers Bureau; Glaxo-Smith Kline: Speakers Bureau.

Impact of Center Experience with Donor Type and Treatment Platform on Outcomes: A Secondary Analysis BMT CTN 1101

BACKGROUND: Our group recently reported on the results of Blood and Marrow Transplant (BMT) Clinical Trials Network (CTN) 1101 a randomized comparison between double umbilical cord blood (dUCB) and haploidentical bone marrow (haplo) with post-transplant cyclophosphamide (ptCy) in the nonmyeloablative setting that showed similar progression free survival (PFS) between the two treatment groups, but lower non-relapse mortality (NRM) and better of overall survival (OS) in the haplo arm. In this secondary analysis we sought to investigate if transplant center experience with haplo and or cord blood HCT had an impact on outcomes. PATIENTS AND METHODS: All patient randomized in BMT CTN 1101 were included. In order to determine the transplant center experience with either haplo or dUCB we queried the Center for International Blood and Marrow Transplant Research (CIBMTR) for number of transplants with each platform in the year prior to initiation of the study. Centers were then grouped as dUCB center (> 10 dUCB, n=117, 10 centers), Haplo center (>10 haplo and ≤10 dUCB, n=110, 2 centers), and ≤10 haplo and ≤10 dUCB HCTs (other center, n=140, 21 centers). Further analysis considered the alternative cut-off for haplo (> 5 vs ≤ 5) experience, and considered the outcomes based on the donor experience vs. others (e.g. dUCB > 10 vs. ≤ 10; haplo > 5 vs. ≤ 5). RESULTS: The effect of center experience on HCT outcomes shown in Figure, below . After adjusting for age, Karnofsky performance score and, disease risk index we found that there was no difference in outcomes between haplo and dUCB for centers that were experienced with dUCB or had limited to no experience with either dUCB or haplo. In contrast, in centers that were primarily experienced with haplo had better outcomes with this donor type, as compared to dUCB. The higher risk of treatment failure (relapse or death) and overall mortality in dUCB in haplo experienced centers was driven by significantly higher risk of relapse. We then considered the transplant experience with each of the donor types separately. In transplant centers that had performed > 10 dUCB, there were similar outcomes for recipients of both dUCB and haplo. Similarly, centers that had ≤ 5 haplo HCTs had no difference in outcomes between donor types suggesting an overlap with centers that had performed > 10 dUCB HCTs. Overall mortality was higher among dUCB recipients in centers that had performed ≤ 10 dUCB. Notably, the hazard ratio of non-relapse mortality favored haplo in all four donor experience type of transplant center, albeit not statistically significant. CONCLUSION: Except for dUCB recipients in centers with < 10 dUCB/year had worse overall mortality, primarily driven by relapse, the transplant center experience in the year prior to the initiation of BMT CTN 1101 had limited impact on the outcomes of this randomized clinical trial. Figure 1 Figure 1. Disclosures Brunstein: NANT: Research Funding; FATE: Research Funding; GamidaCell: Research Funding; BlueRock: Research Funding; AlloVir: Consultancy. Costa: Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria, Speakers Bureau; Pfizer: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria. Cutler: Deciphera: Consultancy; Cimeio: Consultancy; Editas: Consultancy; Kadmon: Consultancy; Pfizer: Consultancy; Mallinckrodt: Consultancy; CareDx: Consultancy; Incyte: Consultancy; Omeros: Consultancy; Syndax: Consultancy; Mesoblast: Consultancy; Jazz: Consultancy. Horowitz: Sobi: Research Funding; Regeneron: Research Funding; Orca Biosystems: Research Funding; Tscan: Research Funding; Xenikos: Research Funding; Miltenyi Biotech: Research Funding; Stemcyte: Research Funding; Medac: Research Funding; Vor Biopharma: Research Funding; Kite/Gilead: Research Funding; Janssen: Research Funding; Pharmacyclics: Research Funding; Kiadis: Research Funding; Seattle Genetics: Research Funding; Mesoblast: Research Funding; GlaxoSmithKline: Research Funding; Sanofi: Research Funding; Pfizer, Inc: Research Funding; Omeros: Research Funding; Magenta: Consultancy, Research Funding; Jazz Pharmaceuticals: Research Funding; Vertex: Research Funding; Genentech: Research Funding; Takeda: Research Funding; Novartis: Research Funding; Shire: Research Funding; Gamida Cell: Research Funding; Daiicho Sankyo: Research Funding; CSL Behring: Research Funding; Chimerix: Research Funding; Bristol-Myers Squibb: Research Funding; bluebird bio: Research Funding; Astellas: Research Funding; Amgen: Research Funding; Allovir: Consultancy; Actinium: Research Funding. Horwitz: Gamida Cell: Research Funding. McGuirk: Novartis: Research Funding; Magenta Therapeutics: Consultancy, Honoraria, Research Funding; EcoR1 Capital: Consultancy; Pluristem Therapeutics: Research Funding; Novartis: Research Funding; Bellicum Pharmaceuticals: Research Funding; Astelllas Pharma: Research Funding; Gamida Cell: Research Funding; Fresenius Biotech: Research Funding; Allovir: Consultancy, Honoraria, Research Funding; Juno Therapeutics: Consultancy, Honoraria, Research Funding; Kite/ Gilead: Consultancy, Honoraria, Other: travel accommodations, expense, Kite a Gilead company, Research Funding, Speakers Bureau. Rezvani: US Department of Justice: Consultancy; Kaleido: Other: One-time scientific advisory board; Nohla Therapeutics: Other: One-time scientific advisory board; Pharmacyclics-Abbvie: Research Funding. Rybka: Spark Therapeutics: Consultancy; Merck: Consultancy. Vasu: Kiadis, Inc.: Research Funding; Boehringer Ingelheim: Other: Travel support; Seattle Genetics: Other: travel support; Omeros, Inc.: Membership on an entity's Board of Directors or advisory committees.

1336. Outcomes of COVID-19 in Recent Kidney Transplants Recipients at a Large Transplant Center in Miami

Background Outcomes of COVID-19 have been reported in deceased donor kidney transplant (DDKT) recipients. However, data is limited in patients that underwent recent DDKT. Methods This single-center retrospective study evaluated the differences in demographics and post-transplant outcomes between those who tested positive and negative for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by polymerase chain reaction, after undergoing recent DDKT. The treatments and outcomes for the SARS-CoV-2-positive patients were assessed. Patients who underwent DDKT from 3/2020 to 8/2020 were included and followed until 9/2020. Results 201 DDKT recipients were analyzed [14(7%) SARS-CoV-2-positive and 187(93%) negative]. There was no difference in delayed graft function and biopsy-proven rejection between both groups. The patient survival at the end of the study follow-up was lower among SARS-CoV-2-positive patients (Table 1). The median time from DDKT to COVID-19 diagnosis was 45 (range: 8-90) days; 5(36%) patients required intensive care unit and 4(29%) required mechanical ventilation; steroids were used in all the patients, therapeutic plasma exchange (TPE) and convalescent plasma (CP) in 7(50%) patients each, remdesivir in 6(43%) and tocilizumab in 1(7%); 9(64%) patients recovered, 3(21%) died and two were still requiring mechanical ventilation at the end of the follow-up. Conclusion Our cohort demonstrated a lower survival rate among SARS-CoV-2-positive patients, which highlights the vulnerability of the transplant population. Transplant patients must comply with the CDC recommendations to prevent COVID-19. Disclosures All Authors: No reported disclosures