Citations

Introduction T-cell prolymphocytic leukemia (T-PLL) is a rare and aggressive malignancy with limited treatment options, frequent relapse, and poor long-term survival. Allogeneic hematopoietic cell transplantation (allo-HCT) is reported to be effective in T-PLL yielding durable remissions and improved survival. Rates of survival and non-relapse mortality vary; data is scant and limited to small retrospective series. Whether regimen intensity - a modifiable factor- is associated with outcomes is unknown. Here, we report an analysis of patients with T-PLL undergoing allo-HCT in 87 centers using data reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). Methods A retrospective multicenter study was conducted utilizing the CIBMTR dataset, which captures all consecutive patients transplanted in participating centers. The study population included patients aged 18 or older with T-PLL who underwent allo-HCT between 2008 and 2018. All donor types, graft sources, and conditioning intensities are summarized (Table 1). Cox regression analysis was used to assess overall mortality, treatment-related mortality (TRM), relapse or progression, disease-free survival (DFS) and acute and chronic graft-versus-host disease (GVHD). Due to the strong correlation between regimen intensity and patients' age, a combined variable was constructed, and in all models, interactions between age/regimen and significant covariates were examined. Results A total of 266 pts were identified and included in this analysis (Table 1). Median follow up of survivors was 49 months (range 3-117); the completeness of survival data out to four years was 95%. In univariate analysis, the 4-year overall survival (OS) rate was 30% (95% confidence interval [CI]: 23.8-36.5%). The 4-year DFS, relapse, TRM rates were 25.72% (95%CI: 20-32%), 41.9% (95%CI: 35.5-48.4%), and 32.4% (95%CI: 26.4-38.6%), respectively. Cumulative incidences of grade 2-4 acute GVHD at day +180 and chronic GVHD at 1-year were 22.5%% (95%CI: 16.8- 28.9%) and 38.3% (95%CI: 32.9-44.9%), respectively. Multivariate analyses was performed using age (Age >60 vs Age ≤60) and conditioning intensity (myeloablative [MAC] vs. reduced intensity/non-myeloablative [RIC/NMA]) as main effect (Table 2). Poor performance status was associated with inferior TRM, DFS and OS. Compared to MAC/Age ≤ 60, RIC/NMA/Age≤ 60 correlated significantly with less TRM (hazard ratio [HR] 0.28, 95% CI 0.14-0.55, p= 0.0022, superior DFS [HR 0.53, 95% CI 0.35-0.81, p= 0.0029; (Figure 3) and OS HR 0.49, 95% CI 0.32-0.75, p= 0.0009; (Figure 2). Similar trend was observed for RIC/NMA/ Age> 60 for all 3 outcomes but did not reach statistical significance. Remission status at time of allo-HCT (stable disease or progression vs. complete remission) correlated with relapse (hazard ratio [HR] of relapse=2.13, 95%CI 1.23-3.71, p=0.0072), however degree of response (partial response vs. complete remission did not significantly correlate with relapse (HR of relapse=1.41, 95%CI 0.91-2.17, p=0.1257). In vivo T cell depletion with ATG (N=47) or alemtuzumab (N=2) was associated with worse TRM [HR 1.82, 95% CI 1.08-3.08, p= 0.0255] and inferior DFS [HR of death or progression=HR 1.50, 95% CI 1.05-2.15, p= 0.0276]. The most common cause of death was disease relapse (52%) followed by infection (15%) and GVHD (13%). Conclusion This CIBMTR study, with the largest cohort of allo-HCT in T-PLL to date, demonstrates allo-HCT is effective in providing durable remissions. MAC and poor performance status were predictive of inferior TRM and OS. Disease relapse was most common cause of death, and pre-allo-HCT remission inversely correlated with relapse rate. The data suggest that reduced intensity conditioning and avoidance of post-transplant ATG/alemtuzumab may be beneficial. Novel strategies are needed both pre and post-allo-HCT to further improve outcomes. Disclosures Dholaria: J&J: Research Funding; bms: Research Funding; Takeda: Research Funding; Poseida: Research Funding; Angiocrine: Research Funding. Jagadeesh:Verastem: Membership on an entity's Board of Directors or advisory committees; Debiopharm Group: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; MEI Pharma: Research Funding; Regeneron: Research Funding. Oran:Celgene: Consultancy; ASTEX: Research Funding; Arog Pharmaceuticals: Research Funding. Nakamura:Viracor: Consultancy; Celgene: Other: Support on seminar; Magenta Therapeutics: Other: Advisory board meeting; NapaJen Pharma: Consultancy; Alexion: Other: Support on a meeting presentation; Merck: Other: advisory board meeting; Kadmon Corporation: Other: Advisory board meeting; Kyowa-Kirin: Other: Support on a meeting presentation. Sauter:Spectrum Pharamaceuticals: Consultancy; Sanofi-Genzyme: Consultancy, Research Funding; Gamida Cell: Consultancy; Novartis: Consultancy; Genmab: Consultancy; Precision Biosciences: Consultancy, Research Funding; Kite - a Gilead Company: Consultancy; Celgene: Consultancy, Research Funding; GSK: Consultancy; Bristol-Myers Squibb: Research Funding; Juno Therapeutics: Consultancy, Research Funding. kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy.

Shear Wave Elastography (SWE) has been used to estimate arterial stiffness, an important biomarker for early cardiovascular diseases. In arterial SWE, the carotid artery is excited with acoustic radiation force and the observed wave propagation characteristics are used to estimate the arterial stiffness. We present an efficient guided wave inversion procedure, where the elastic modulus of the arterial wall is estimated by matching wave dispersion from experiments and simulation. Central to the proposed approach is an efficient forward model that computes the dispersion relation for the incompressible, immersed cylindrical tube; the model achieves high efficiency by combining semi-analytical finite element methods, selective reduced integration, and perfectly matched discrete layers. We utilize this forward model in an optimization framework to develop a streamlined methodology to estimate the modulus of the arterial wall. In this talk, we present the formulation of the method as well as validation using phantom experiments on 10 rubber tubes (VytaFlex-10), followed by preliminary work on inverting for the viscoelastic modulus.

Abstract Background/Introduction TAILOR-PCI is the largest cardiovascular genotype-based randomized trial (NCT#01742117) investigating whether genotype-guided selection of oral P2Y12 inhibitor therapy improves ischemic outcomes after percutaneous coronary intervention (PCI). The TAILOR-PCI Digital Sub-Study tests the feasibility of extending original follow-up of 1 year to 2 years using state-of-the-art digital solutions. Deep phenotyping acquired during a clinical trial can be leveraged by extending follow-up in an efficient and cost-effective manner using digital technology. Purpose Our objective is to describe onboarding and engagement of participants initially recruited in a large, pragmatic, international, multi-center clinical trial to a digital registry. Methods TAILOR-PCI participants, within 23 months of their index PCI, were invited by letters containing a URL to the Digital Sub-Study website (http://tailorpci.eurekaplatform.org). These invitations were followed by phone calls, if no response to the letter, to determine reason for non-participation. A NIH-funded direct-to-participant digital research platform (the Eureka Research Platform) was used to onboard, consent and enroll participants for the digital follow-up. Participants were asked to answer health-related surveys at fixed intervals using the Eureka mobile app and desktop platform. To capture hospitalizations, participants could enable geofencing to allow background location tracking, which triggered surveys if a hospitalization was detected. Result(s) Letters were mailed to 893 of 929 eligible participants across 22 sites in the United States and Canada leading to 226 homepage visits and 118 registrations. There were 107 consents (12.0% of invited; mean age: 66.4±9.0; 19 females [18%]): 47 (44%) participants consented after the letter, 36 (34%) consented after the 1st call and 24 (22%) consented after a 2nd call. Among those who consented, 100 were eligible (7 did not have a smartphone) 81 downloaded the study mobile app and 73 agreed for geofencing (Figure 1). Among the 722 invited participants who were surveyed, 354 declined participation: due to lack of time (146; 20.2%), lack of smartphone (125; 17.3%), difficulty understanding (41; 5.7%), concern about using smartphone (34; 4.7%), concern of data privacy (14; 1.9%), concerns of location tracking (6; 0.8%) and other reasons (57; 7.9%). Conclusion Extended follow-up of a clinical trial using a digital platform is feasible but uptake in this study population was limited largely due to lack of time or a smartphone among participants. Based on data from other digital studies, uptake may also have been limited since digital follow-up consent was not incorporated at the time of consent for the main trial. Figure 1. Onboarding of the digital substudy Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Institute of Health (NIH), National Heart, Lung, and Blood Institute (NHLBI)

Abstract Carfilzomib was approved for the treatment of multiple myeloma in 2012 and since then there have been concerns for cardiovascular toxicity from its use. With this study, we aim to further study the hazards and underlying risk factors for cardiovascular adverse events associated with carfilzomib. This study was conducted using Surveillance, Epidemiology, and End Results (SEER)‐Medicare data set of multiple myeloma from 2001 to 2015. Data were analyzed for hazards ratio of cardiovascular adverse events between carfilzomib users and nonusers. We identified 7330 patients with multiple myeloma of whom 815 were carfilzomib users. Carfilzomib users had a statistically significant hazard ratio of 1.41 with p < 0.0001 for all cardiovascular adverse events as compared to nonusers. Carfilzomib use was significantly associated with increased risk of heart failure (HR 1.47, p = 0.0002), ischemic heart disease (HR 1.45, p = 0.0002), and hypertension (HR 3.33, p < 0.0001), whereas there was no association between carfilzomib use and cardiac conduction disorders (arrhythmia and heart blocks). Carfilzomib users were at higher risk of new‐onset edema (HR 5.09, p < 0.0001), syncope (HR 4.27, p < 0.0001), dyspnea (HR 1.33, p < 0.0001), and chest pain (HR 1.18, p < 0.0001) as compared to carfilzomib nonusers. Age above 75 years, preexisting cardiovascular disease, obesity, and twice a week carfilzomib schedule were significant risk factors associated with cardiovascular adverse events in carfilzomib users. The median time of the onset for all cardiovascular adverse events was 3.1 months. This study has identified a significantly higher likelihood of cardiovascular adverse events in elderly Medicare patients receiving carfilzomib.

Background: Allogeneic HCT continues to be the optimal consolidation strategy for many patients with AML. Cytogenetic and molecular abnormalities are known to influence post-transplant outcomes. We tested the prognostic ability of a modified (mELN) classification system based on available CIBMTR genetic data, to predict post-transplant outcomes. Methods: Adult patients with a diagnosis of AML in first complete remission (CR1) with available pre-transplant cytogenetic and molecular mutational data, receiving a first allogeneic HCT from 2013-2017, were included. Patients were stratified according to mELN genetic classification in three distinct groups: favorable (Fav), intermediate (IM) and adverse (Adv). Clinical outcomes following HCT were compared among groups after adjusting for significant patient, disease, and transplant-related variables. The primary endpoint was disease free survival (DFS). Secondary endpoints were overall survival (OS), non-relapse mortality (NRM), cumulative incidence of relapse, acute GVHD, and chronic GVHD. Cox proportional hazard models were used to compare endpoints among mELN risk groups, age groups and genetic subsets within the adverse-risk group. Results: Demographic characteristics are summarized in Table 1. 2289 patients (Fav, n=181; IM n=1185; and Adv n=923) met the inclusion criteria. Median follow-up for survivors was 35 months. Importantly, 41% of transplant recipients (n=936) were &gt;60 years, 76% (n=1743) had de novo AML and 48% (n=1111) received a myeloablative conditioning regimen. Univariate analysis (UVA) demonstrated significant differences in 2-year OS (Fav: 67.7%, IM: 64.9% and Adv: 53.9%; p&lt;0.001); DFS (Fav: 57.8%, IM: 55.5% and Adv: 45.3; p&lt;0.001) and relapse (Fav: 28%, IM: 27.5% and Adv: 37.5%; p&lt;0.001) There were no significant differences in NRM (p=0.467) or the incidence of acute (p=0.423) and chronic GVHD (p=0.442) among mELN groups. Initial multivariate analysis (MVA) of mELN risk groups indicated that there was no significant difference in clinical outcomes between the Fav and IM risk groups. Thus, these groups were combined for subsequent analyses. Adv risk (vs. Fav/IM) led to significantly worse OS (HR 1.39 [1.24-1.57] p=&lt;0.001), DFS (HR 1.32 [1.18-1.48] p=&lt;0.001), and relapse (HR 1.42 [1.23-1.63] p=&lt;0.001) (Table 2). This mELN classification effectively stratified both younger (&lt;60 y/o) and older (&gt;60 y/o) patients for OS (Adv vs. Fav/IM HR for &lt;60: 1.43 [1.21-1.69] p&lt;0.001; &gt;60: 1.44 [1.21-1.72] p&lt;0.001), DFS (Adv HR for &lt;60: 1.31 [1.12-1.53] p&lt;0.001; &gt;60: 1.41 [1.19-1.66] p&lt;0.001) and relapse (Adv HR for &lt;60: 1.44 [1.20-1.74] p&lt;0.001, &gt;60: 1.42 [1.15-1.76] p=0.001). NRM was higher for older patients (&gt;60 y/o) in both the Fav/IM (HR 1.40 [1.10-1.78] p=0.007) and Adv-risk cohorts (HR 1.59 [1.18-2.13] p=0.002). Genetic subset comparisons within the adverse-risk group showed that patients carrying monosomy 5, del(5q) or monosomy 7 had inferior 2-year OS (42.6%, p&lt;0.001) and DFS (35.2%, p&lt;0.001), as well as higher rates of relapse (45%, p= 0.002) when compared to other patients within the Adv-risk cohort (OS 60%, DFS 50.8%, relapse 33.5%). Conclusion: Stratification using mELN criteria resulted in clear prognostic separation of OS, DFS and relapse in this large cohort of AML patients undergoing allogeneic HCT. While Fav and IM groups had similar OS, DFS and relapse rates; patients in the Adv risk group had the highest risk of relapse and inferior DFS/OS. However, the majority of patients in all cohorts had favorable outcomes for HCT in CR1. Our findings confirm the value of a combined genetic prognostic model in the AML HCT setting and justify the use of this stratification system in future HCT trials. Correlation of genetic subtypes with other important transplant variables, such as conditioning intensity and pre-transplant MRD status deserves further evaluation. There remains a subset of Adv-risk patients for which post-transplant outcomes continue to be poor, even when transplanted in CR1. Novel peri-transplant pre-emptive/therapeutic strategies are urgently needed for this high-risk cohort. Disclosures de Lima: Celgene: Research Funding; Pfizer: Other: Personal fees, advisory board, Research Funding; BMS: Other: Personal Fees, advisory board; Incyte: Other: Personal Fees, advisory board; Kadmon: Other: Personal Fees, Advisory board. Komanduri:Kiadis: Consultancy; Takeda: Consultancy; Celgene: Consultancy; Atara: Consultancy, Membership on an entity's Board of Directors or advisory committees; Adaptimmune: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees. Kebriaei:Ziopharm: Other: Research Support; Jazz: Consultancy; Kite: Other: Served on advisory board; Amgen: Other: Research Support; Novartis: Other: Served on advisory board; Pfizer: Other: Served on advisory board. Bhatt:National Marrow Donor Program: Research Funding; Rigel Pharmaceuticals: Other; Jazz: Research Funding; Agios: Other: Personal Fees; Incyte: Other: Personal Fees, Research Funding; Takeda: Other: Personal Fees; Partner Therapeutics: Other: Personal Fees; Pfizer: Other, Research Funding; CSL Behring: Other; Tolero Pharmaceuticals: Research Funding; Oncoceutics: Other: Drug support for a trial; Partnership for health analytic research, LLC: Other: Personal Fees; Omeros: Other: Personal Fees; Abbvie: Other: Personal Fees, Research Funding. Saad:Orcabio: Other: research support; Kadmon: Other: research support; Amgen: Other: research support; Incyte Pharmaceuticals: Other: Personal Fees; Magenta Therapeutics: Other: Personal Fees. Copelan:Amgen: Membership on an entity's Board of Directors or advisory committees. Defilipp:Incyte: Research Funding; Regimmune: Research Funding; Syndax Pharmaceuticals: Consultancy. Nishihori:Karyopharm: Other: Research support to institution; Novartis: Other: Research support to institution. Weisdorf:Incyte: Research Funding; FATE Therapeutics: Consultancy.

Introduction: Cryopreservation of peripheral blood stem cell (PBSC) or bone marrow (BM) grafts is rarely performed, thus information about the effect of cryopreservation on graft characteristics and HCT outcomes is limited. Given the global pandemic leading to changing practices for donor graft collection and increasing utilization of cryopreservation, we evaluated the outcomes of HCT recipients who received either fresh or cryopreserved allogeneic BM or PBSC grafts. The primary endpoint was engraftment. Secondary endpoints were incidence of acute graft-vs.-host disease (aGVHD), relapse, transplant related mortality (TRM), disease free survival (DFS), and overall survival (OS). Methods: We included 7397 patients transplanted between 2013 and 2018. 1883 cryopreserved graft recipients were divided into three cohorts based on graft source and donor type: matched related (MRD) PBSC (n=1,051), matched unrelated (MUD) PBSC (n=678), and matched related or unrelated bone marrow donors (n=154). All patients received conventional calcineurin-based GVHD prophylaxis strategies. Patients who received cryopreserved grafts were matched with 5514 patients who received fresh adjusting for graft type (BM vs. PB), donor source (MRD vs. 8/8 MUD), Disease Risk Index (DRI, low risk vs. intermediate risk vs. high risk), recipient age (within 5-years) and propensity score (within 1 standard deviation from pooled sample). The propensity score was based on age, Karnofsky score, diagnosis, disease risk index, HCT-comorbidity index, and conditioning intensity. The level of statistical significance for the main analyses was p&lt;0.01 due to multiple comparisons. The reason for cryopreservation was available on a subset of URD product recipients. Results: Baseline characteristics for all cohorts are shown in Table 1. CD34+ doses reflect enumeration at infusion. In univariate analyses, rates of graft failure and neutrophil recovery at day 28 were similar for cryopreserved and fresh grafts for BM or related PBSC recipients but differed in cryopreserved vs. fresh MUD PBSC (5% vs 2%, p&lt;0.001 and 87 vs 94%, p&lt;0.001, respectively). Rates of platelet recovery within 28 days were similar in marrow cohorts, but lower with cryopreserved related (92% vs 96%, p&lt;0.001) and MUD PBSC (87 vs 94%, p&lt;0.001) grafts. In matched pair multivariable analyses, there were no significant differences in any outcomes with cryopreserved vs fresh BM grafts, irrespective of donor type. With related donor PBSC, cryopreservation was associated with decreased platelet recovery (HR=0.73, CI=0.68-0.78, p&lt;0.001) and an increased risk of both grade II-IV (HR=1.27, CI=1.09-1.48, p=0.002) and grade III-IV (HR=1.48, CI=1.19-1.84, p&lt;0.001) aGVHD, but had no impact on other outcomes. In contrast, with MUD PBSC grafts, cryopreservation was associated with delayed engraftment of neutrophils (HR=0.77, CI=0.71-0.84, p&lt;0.001) and platelets (HR=0.61, CI=0.56-0.66, p&lt;0.001), an increased risk of TRM (HR=1.4, CI=1.18-1.66, p&lt;0.001) and relapse (HR=1.32, CI=1.11-1.58, p=0.002), and decreased DFS (HR=1.36, CI=1.20-1.55, p&lt;0.001) and OS (HR=1.38, CI=1.22-1.58, p&lt;0.001) but did not affect the incidence of aGVHD. In a subset analysis of 299 URD recipients, the most common reason for cryopreservation was delays due to patient-related events, eg., additional chemotherapy, infection. These recipients had decreased OS compared to products cryopreserved for non-patient reasons (HR=0.65, CI=0.44-0.96, p=0.029). Conclusions: The retrospective nature of this analysis, and the fact that cryopreservation is likely to have been performed in recipients with a different (higher) risk profile compared to those receiving fresh products limits our ability to draw firm confusions. Despite these limitations, we conclude that cryopreservation in some patient populations may have a negative impact on engraftment and transplant outcomes. The decision to cryopreserve donor grafts, particularly PBSC grafts, should be carefully considered and highlights the need for further investigation of the effect of cryopreservation of allogeneic grafts where patient factors can be controlled for. Disclosures Farhadfar: CSL Behring: Research Funding; Incyte pharmaceutical: Other: Member of GVHD advisory forum. Frey:Kite Pharma: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Syntax: Consultancy, Honoraria. Devine:Magenta Therapeutics: Consultancy. Shaw:Orca Bio: Consultancy. Wingard:Shire: Other: Personal Fees - serve on data monitoring committee for clinical trial; Ansun: Other: Personal Fees - serve on data monitoring committee for clinical trial; Merck: Other: Personal Fees - serve on data monitoring committee for clinical trial; Cidara: Other: Personal Fees - serve on data monitoring committee for clinical trial; ReViral: Other: Personal Fees - serve on data monitoring committee for clinical trial.

Introduction The molecular basis for the clinical heterogeneity observed in DLBCL was initially elucidated with gene expression profiling, identifying three distinct subgroups with prognostic relevance based on COO namely activated B-cell like (ABC), germinal center B-cell like (GCB) and a type 3 subtype. Shortly afterwards the Han's algorithm was developed allowing for widespread applicability and correlation of gene expression with protein expression. GCB profile at diagnosis is generally associated with superior long term outcomes compared to non-GCB in patients treated with conventional chemo-immunotherapy. Patients with relapsed/refractory disease (R/R) after front-line therapy undergo salvage treatment and, upon demonstration of objective response, proceed with high dose therapy and autologous transplantation (auto-HCT). Whether COO by Han's algorithm retains its prognostic significance post auto-HCT in patients with R/R DLBCL is not well established. Methods A retrospective analysis was carried out using the Mayo database across all three sites. It included all patients between 18-75 years of age who were diagnosed with de-novo DLBCL and underwent first auto-HCT for R/R disease. Patients must have received salvage treatment and shown chemotherapy sensitive disease. Patients with transformed DLBCL or other variants and those who underwent auto-HCT in first complete remission for high risk disease were excluded. Patients who had less than a partial response at time of transplantation and still underwent auto-HCT were also excluded. Primary endpoints included overall survival (OS); relapse and relapse-free survival (RFS). Continuous variables were summarized as mean (standard deviation) and median (range). Categorical variables were reported as frequency (percentage). Continuous baseline variables were compared between GCB and non-GCB using Wilcoxon rank sum test and categorical baseline variables were compared with Chi-squared test. Kaplan-Meier method was used to estimate 5 and 10-year rates of freedom from long-term events and draw corresponding survival curves. Multivariate Cox regression models were used to evaluate the impact of cell of origin on long-term survival after adjusting for baseline factors. All tests were two-sided with alpha level set at 0.05 for statistical significance. Results A total of 357 patients underwent auto-HCT between 2005 and 2018. Cell of origin was determined for 284 patients and these were included in the analysis. Median age was 61 (range: 19-78) years, and 64% patients were male. GCB and non-GCB patients did not differ significantly with regards to baseline factors (Table 1). During a median follow up of 1.7 years, 139 patients died, 151 had relapse and 175 either died or had relapse. Five year OS for GCB patients versus (vs) non-GCB was at 44% vs 64% (p=0.004) (Figure 1); relapse was at 67% vs 49% (p=0.012) and RFS was at 28% vs 50% (p=0.003). The difference between GCB and non-GCB groups remained statistically significant in multivariate analysis, with GCB patients 60% more likely to die and 50% more likely to experience relapse compared to non-GCB patients. The only other factor that retained significance in multivariate analysis for both endpoints was response at the time of transplant (Table 2A-C). Conclusions In this analysis, GCB DLBCL results in worse OS and higher relapse vs. non-GCB DLBCL following an auto-HCT. It is unclear if relapse/progression of GCB subtype after front-line therapy alters its natural history into a more aggressive disease. Disclosures Ansell: Trillium: Research Funding; ADC Therapeutics: Research Funding; Affimed: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding. Foran:Xencor: Research Funding; Trillium: Research Funding; Takeda: Research Funding; Kura Oncology: Research Funding; Aptose: Research Funding; Aprea: Research Funding; Actinium: Research Funding; Boehringer Ingelheim: Research Funding; Abbvie: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Revolution Medicine: Consultancy; H3Biosciences: Research Funding; Agios: Honoraria, Research Funding. Tun:Celgene: Research Funding; Mundipharma: Research Funding; Curis: Research Funding; TG Therapeutics: Research Funding; Acrotech: Research Funding; DTRM Biopharma: Research Funding; Bristol-Myers Squibb: Research Funding. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy.

AbstractWe studied 2,528 patients with upfront autologous haematopoietic cell transplantation (AHCT) for multiple myeloma (MM) from 2008–2017 to develop a prognostic model to predict outcomes. High‐risk cytogenetics included t(4;14), t(14;16), t(14;20), del13q on karyotype, del17p, +1q or 1pdel. A Cox model identified factors prognostic of progression/relapse in a training subset (n = 1,246). A weighted score using these factors was assigned to a validation cohort (n = 774). Presence of high‐risk cytogenetics [hazard ratio, (HR) 1·68 (1·3–2·17)] and pre‐AHCT bone marrow plasma cells (BMPCs) ≥10% [1·68 (1·33–2·12)] were assigned 4 points each; albumin at diagnosis <3·5 g/dl [1·31 (1·07–1·61)] 2; standard risk cytogenetics 1, and no cytogenetics abnormality, BMPCs <10% at AHCT and albumin ≥3·5 g/dl at diagnosis 0 points each. A three‐category system with low risk (0–3), intermediate risk (4–8) and high risk (9–10) showed 3‐year progression‐free survival in the low vs. intermediate vs. high risk of 58% (95% CI: 52–63) vs. 49% (95% CI: 43–56) vs. 31% (95% CI: 12–51), P < 0.001 respectively, and 3‐year OS in low vs. intermediate vs. high risk of 88% (95% CI: 84–91) vs. 81% (95% CI: 76–86) vs. 64% (95% CI: 39–80); P < 0·001. Our prognostic scoring system can identify MM patients at risk for early relapse after AHCT.