Giri, Smith

Introduction Talquetamab (Tal) is a bispecific antibody therapy approved for patients with relapsed/refractory multiple myeloma (MM), targeting both GPRC5D x CD3 receptors. Clinical studies demonstrate that Tal creates deep and durable responses, but less is known about the patient experience. This study aimed to explore the experiences of patients from the patient and physician perspectives to help understand what to expect with this first-in-class novel therapy. Methods This qualitative study consisted of two components. First, patients with documented use of Tal were identified and recruited through the HealthTree Cure Hub. Individual patients then took part in 2-hour, semi-structured virtual interviews. Patients were asked to discuss their experiences 1-2 months prior to starting Tal and 4-5 months after beginning therapy. Patients discussed GPRC5D-related symptoms (including oral, skin-related, and nail-related symptoms), overall impact, and satisfaction. Participants were US adults diagnosed with MM who took Tal for at least 4 months, either in a clinical trial or commercial setting. Interviews were conducted February-April 2024. Second, patients and physicians gathered in a facilitated roundtable discussion to review findings and generate ideas for symptom management. The roundtable lasted 2.5 hours and took place in Chicago, IL in May 2024. Results Eight men and 4 women (n=12) aged 52-79 years participated in the interviews. Interviewed participants were mostly white (84%) and college-educated (84%). Seven (58%) received Tal in a clinical trial, and 9 were still receiving Tal. Roundtable participants included 3 patients (2 male, 1 female; all white and college educated), and 3 physicians with Tal experience. In the interviews, patients reported high overall satisfaction, with most (75%) rating their satisfaction an 8 or higher on a 10-point scale. Satisfaction was largely driven by the effectiveness of therapy, with all reporting clinical improvements in their MM disease. Patients reported improvements in their energy/strength and decreased fatigue. They also reported increased physical activities and ability to work, volunteer, or engage in hobbies. Physicians affirmed these findings as consistent with their observations and noted they are also very satisfied with Tal. Oral symptoms such as distorted taste/loss of taste and dry mouth were common with varying severity. Although all interviewees (n=12) reported taste symptoms (typically starting within the first month of therapy), five said their symptoms fully improved (3 after 1 year on treatment, and 2 after stopping treatment), and 4 experienced partial recovery. Ten lost weight, but most (92%) maintained adequate nutrition. Patients and physicians suggested coping strategies including diet modifications, use of strong flavors (e.g., spices, cinnamon), monitoring weight, prioritizing adequate calories, using mouthwashes/lozenges, drinking water while eating, taking small bites, and consuming foods that are easier to swallow. They also noted that it is important to educate patients on expected symptoms before starting the treatment. Skin- and nail-related symptoms were also common but less impactful on quality of life. All patients (n=12) experienced skin- and nail-related symptoms, but most considered these symptoms mild. Skin-related symptoms such as dry skin and skin exfoliation occurred once and typically lasted 1-2 months. Nail symptoms were primarily cosmetic, and the duration varied. Suggested management strategies include skin moisturizers, nail thickeners/hardeners, keeping nails short, and wearing protective gloves. Physicians noted it's helpful to reassure patients that nail loss is not harmful. Overall, GPRC5D-related symptoms were not treatment-limiting, and no patients had to miss a dose due to these symptoms. Patients perceived these symptoms as bearable but noted the treatment was effective at controlling the disease. Conclusions This study described patient experiences and reflected how patients perceive the benefit/risk profile of talquetamab. Symptoms were perceived as bearable and high satisfaction scores were driven by strong clinical effectiveness. Patients and physicians agreed that real-world feedback from patients, practical tips, and management strategies are vitally important for patients and physicians using this novel therapy.

Introduction: Progression-free survival (PFS), a common endpoint used in multiple myeloma (MM) trials, is defined as the time to the earliest occurrence of disease progression or death. Disease progression in MM is determined by the International Myeloma Working Group (IMWG) Uniform Response Criteria based on imaging and biomarkers from blood, urine, and bone marrow biopsy testing. In clinical trials, IMWG-recommended assessments are protocol-based. However, in routine care, they are often performed and recorded differently based on a multitude of factors. Algorithms to ascertain progression events using routinely collected biomarker data are important to classify disease progression and align with trial-defined endpoints. The aim of this study was to construct and evaluate an optimized real-world Progression (rwP) algorithm using real-world data (RWD) and demonstrate how biomarker data are captured and used to derive endpoints in routine practice. Method: A cohort of 270 transplant ineligible newly diagnosed MM (NDMM) patients starting on lenalidomide and dexamethasone (Rd) and meeting select MAIA (NCT02252172) trial eligibility criteria were included from the nationwide Flatiron Health electronic health record-derived deidentified database from 2015 to 2023. A rwP algorithm was developed using key IMWG-recommended biomarkers, including serum protein electrophoresis (SPEP), urine protein electrophoresis (UPEP), and serum free light chains (FLCs). The initial baseline value for each biomarker was identified, if available, during either the pre-treatment period (45 days prior through 7 days after first line treatment (1L) start) or the on-treatment period (8 days after 1L start through 1L end). Available biomarkers were followed for disease assessment. If any subsequent lab value during 1L treatment was less than the initial baseline value, the subsequent “on-treatment baseline” value was adopted as the new lowest value; otherwise, the initial value was used as the “on-treatment baseline” value for assessing progression events. Three hierarchies, which describe the order of utilizing key IMWG-recommended biomarkers to define rwP events, were explored, including: i) strict hierarchy (SH) [SPEP > UPEP > FLCs], ii) partial hierarchy (PH) [SPEP = UPEP > FLCs], and iii) no hierarchy (NH) [SPEP = UPEP = FLCs]. The PH option closely resembles the IMWG criteria, with FLCs only used for rwP assessments when no measurable SPEP/UPEP are identified. The NH approach considers all available biomarkers equally and resembles how they are utilized in real-world clinical practice. The feasibility of requiring confirmatory events following the first suggestion of progression was also explored. Assays following the progression event up to the end of 1L were considered as confirmatory progression events if they met IMWG criteria. Results: We applied the rwP algorithm with each hierarchy to all eligible NDMM patients (N = 270). The SH, PH, and NH approaches respectively identified 106 (39.3%), 106 (39.3%), and 123 (45.6%) patients with progression events. Results from the SH and PH approach are consistent likely due to the rarity of UPEP usage in clinical practice. The NH approach captured more progression events because FLCs are used in the presence of measurable SPEP or UPEP, resulting in 73 progression events captured by FLCs in comparison to 32 from other approaches, potentially increasing false-positive capture due to the volatility of FLC results. Among the 106 PH-identified patients who progressed, 84 (79.2%) had IMWG-recommended tests following their progression event, and 74 (69.8%) had their progression confirmed before the end of 1L, with 35 (33.0%) receiving their confirmatory reading within 2 months after progression. These results suggest that requiring confirmatory testing as part of the rwP is feasible, though it may introduce false negatives for patients receiving clinical progression confirmation instead of getting confirmatory labs in routine clinical practice. Conclusion: We present a comprehensive assessment of important design choices for an rwP algorithm for NDMM, including a hierarchy of key assays and the inclusion of confirmatory testing. Further validation of the algorithm with health care provider assessments and the inclusion of bone marrow biopsy and imaging data is important for evaluating the performance of the algorithm in RWD.

Background The International Myeloma Working Group (IMWG) defines progression of disease (PD) in multiple myeloma by the increase in serum or urine paraprotein beyond a certain threshold. With more common use of serial bone marrow-based measurable residual disease (MRD) assessment, some patients will evolve with increase in MRD burden without meeting IMWG criteria for PD and no consensus exist on the best approach for this circumstance. Understanding the implication of increasing MRD burden is particularly relevant in patients with NDMM treated with quadruplet therapy (QUAD, consisting of a PI, an IMiD, an anti-CD38 mAb and dexamethasone) and autologous stem cell transplantation (ASCT), since most will reach MRD negativity. Methods We analyzed a large dataset of patients with NDMM patients treated with QUADs + ASCT +/- QUAD consolidation +/- single or dual-agent maintenance and monitored with serial MRD assessment by next-generation sequencing (clonoSEQ®) at end of induction, post ASCT, post consolidation and at least yearly thereafter. The dataset was built from clinical trial participants and from patients receiving standard care under an institutional guideline. MRD progression (MRD-P) was arbitrarily defined as quantitative increase in MRD burden by at least 1 log10 above the nadir. Treatment modification in the event of MRD-P without IMWG-defined PD was at discretion of treating physician. For patients with MRD-P without concomitant PD we describe cumulative incidence of PD after MRD-P. To contextualize the clinical implications of MRD-P, we describe survival free of failure of second line therapy (SFF2T), defined as time from progression event (MRD-P or PD not preceded by MRD-P) until occurrence of IMWG-defined PD or death on second line therapy, regardless of whether second line was initiated due to MRD-P or subsequent IMWG-defined PD. Results Among the 216 patients, 113 (52%) were clinical trial participants, median age was 61 years (IQR 56-68), 21% age 70 or older, 32% were non-White, 57% male, 38% had MM with 1 high risk chromosome abnormality [HRCA, gain/amp 1q, t(4;14), t(14:16), del(17p)], 15% had 2+HRCA. Median duration of follow up is 39.5 months, and 966 MRD datapoints were assessed. Overall, 83% of patients achieved sCR, 78% achieved MRD negativity at 10-5, 68% at the 10-6 threshold, and 56% ceased therapy after confirmed MRD negativity. Forty-nine patients had MRD-P(N=19) or PD not preceded by MRD-P (N=30). Patients with MRD-P or PD not preceded by MRD-P often had 1 or more HRCA (73.7% and 82.8%), International staging system III (36.8% and 30.0%), and high LDH at diagnosis (31.6% and 23.3%). Median time from onset of therapy to MRD-P was 24.4 mo (IQR 12.6-35.8) and to PD not preceded by MRD-P was 23.2 mo (IQR 18.4-29.4). Among the 19 patients with MRD-P, 12 (63%) were on observation at time of MRD-P, 2 were receiving single agent lenalidomide, 5 were receiving a QUAD, 17 (89%) had previously obtained MRD negativity at <10-5 including 10 (53%) who had also achieved MRD negativity at 10-6. Upon detection of MRD-P, 12 (63%) pts changed therapy, all to a monoclonal antibody-containing triplet or quadruplet regimen. Yet, median time from MRD-P to PD was 10.1 months. Patients with MRD-P had 1-year SFF2T of 55.6%, in contrast to 35.0% for patients with PD not preceded by MRD-P. OS 2-year from progression event was 78.0% for MRD-P and 53.9% for PD not preceded by MRD-P. Conclusions In the context of NDMM treated with QUAD + ASCT, MRD-P indicates imminent IMWG-defined PD, and is driven by a thriving plasma cell population unlikely to be controlled by reinstitution of anti-CD38 mAb-based combinations. Outcomes of patients with MRD-P are similar to those of patients with IMWG-defined PD. These findings challenge the paradigm of IMWG-defined PD and paraprotein measurability as minimal parameters for change in therapy and introduction of agents with new mechanisms of action.

Introduction QUADs with an anti-CD38 mAb, an IMiD, a proteasome inhibitor and dexamethasone followed by autologous stem cell transplantation (ASCT) in the treatment of newly diagnosed multiple myeloma (NDMM) led to substantial improvements in depth of response, including minimal residual disease (MRD) negativity, and progression-free survival. The therapeutic success of QUAD + ASCT has reinvigorated an interest in fixed-duration therapy, yet optimal short term efficacy endpoint for treatment cessation is unknown. We used a large dataset of patients (pts) treated with QUAD + ASCT, monitored with serial MRD assessment and managed with MRD-adapted treatment duration to determine the best short term efficacy endpoint to identify pts at low risk of treatment failure. Methods We analyzed 221 pts with NDMM treated with QUAD + ASCT and monitored with serial marrow-based MRD by next-generation sequencing. Based on trial design (N=116) and institutional practice (N=105) most pts received post ASCT QUAD consolidation followed by treatment cessation if MRD<10-5 in two consecutive datapoints. Pts were monitored with MRD post induction, ASCT and consolidation, and at least yearly thereafter. We utilized Cox regression to build parallel models of predictors of disease progression or MRD resurgence utilizing the same patient and disease features and one distinct short term efficacy endpoint per model, inputted as time-varying covariate. Efficacy endpoints tested were IMWG-defined stringent complete response (sCR), MRD<10-5 (single datapoint), MRD<10-6, sustained MRD (S-MRD, two consecutive assessments at least 1 year apart) <10-5and S-MRD<10-6. Best fitting models were determined using Akaike's information criterion (AIC, lower value indicates better fitting model), an information theory approach, and Heagerty & Zheng C index. Results Median age of the 221 pts. was 61 years (IQR 55-68), 21% age 70 or older, 37% had MM with 1 high risk chromosome abnormality [HRCA, gain/amp 1q, t(4;14), t(14:16), del(17p)], 16% had 2+HRCA, 30% were non-White and 58% male. We followed pts for a median of 40.6 mo. and performed 979 MRD assessments. One hundred and eighty-three pts (83%) achieved sCR, 170(77%) MRD<10-5, 146 (66%) MRD<10-6 , 110 (50%) S-MRD<10-5 and 90 (41%) S-MRD<10-6. Average time to achieve the endpoint was 7.8 mo for sCR, 7.1 mo. for MRD <10-5, 9.4 mo. for MRD<10-6, 19.3 mo. for S-MRD<10-5 and 20.5 mo. for S-MRD<10-6. We first tested models to predict PFS among all 221 pts. Predictive models included same covariates (ISS, high LDH and number of HRCA) other than the efficacy endpoint. The best fitting model (AIC 417.2, C-stat 0.757) was based on S-MRD<10-5 (HR=0.23, 95% C.I. 0.11-0.47) and included presence of 1 HRCA (HR=2.93, 95% C.I. 1.31-6.52), 2+ HRCA (HR=7.60, 95% C.I. 3.31-17.43), high LDH (HR=1.64, 95% C.I. 0.81-3.33), ISS2 (HR 1.31, 95% C.I. 0.62-2.77) and ISS3 (HR 1.96, 95% C.I. 0.90-4.28). The model with S-MRD<10-6 had similar, but not better performance (AIC 419.4, C-stats 0.752). We subsequently analyzed the 121 (55%) pts who reached MRD-guided treatment cessation for the best fitting model to predict progression or MRD resurgence in absence of continuous therapy. Among these pts, 117(97%) achieved sCR, all (by definition) MRD<10-5, 116 (96%) MRD<10-6 , 95 (79%) S-MRD<10-5 and 81 (67%) S-MRD<10-6. The best fitting model (AIC 197.2, C-stat 0.766) was based on S-MRD<10-5 (HR 0.01, 95% C.I. 0.002-0.05) and included presence of 1 HRCA (HR=1.41, 95% C.I. 0.49-4.07), 2+ HRCA (HR=5.43, 95% C.I. 1.75-16.84), high LDH (HR=3.19, 95% C.I. 0.98-10.40), ISS2 (HR 1.25, 95% C.I. 0.53-2.96) and ISS3 (HR 0.73, 95% C.I. 0.23-2.37). The model with S-MRD<10-6 did not perform better (AIC 232.4, C-stat 0.727). Among the 94 pts who achieved S-MRD<10-5 and were monitored off therapy, the risk of MRD resurgence or progression 2.5 years after cessation of therapy was 6.1%, 15.0% and 22.2% and risk of progression was 3.0 %, 12.0% and 11.1% for patients with 0, 1 and 2+ HRCA, respectively Conclusion In the setting of QUAD therapy + ASCT, no short term conventional or MRD-based efficacy parameter negates the cumulative impact of HRCA on prognosis. S-MRD<10-5 is the best predictor of PFS and yields the best predictive models for risk of MRD resurgence or progression in the setting of fixed duration therapy. This analysis validates S-MRD<10-5 as efficacy parameter for treatment cessation in trials exploring fixed duration therapy in NDMM.

Abstract Data evaluating change in body composition during treatment of advanced cancer are limited. Here we evaluated computed tomography (CT)–based changes in muscle mass during treatment for advanced ovarian cancer (OC) and association with outcomes. We analyzed the preoperative and posttreatment skeletal muscle index (SMI), skeletal muscle area normalized for height of 109 patients with advanced OC who underwent primary surgery and platinum-based chemotherapy from 2006 to 2016. Based on an SMI less than 39 cm2/m2, 54.1% of patients were never sarcopenic, 24.8% were sarcopenic on both CT scans, and 21.1% were newly sarcopenic upon treatment completion. Patients who lost muscle during treatment had the worst survival of the 3 groups identified: median survival 2.6 years vs 4.6 years if sarcopenic on both CT scans and 4.8 years if never sarcopenic. Loss of muscle portends a poor prognosis among patients with OC. Additional research is needed to better understand and best mitigate these changes.

Abstract Background: MM that is refractory to a proteasome inhibitor (PI), an immunomodulatory agent (IMiD) and an anti-CD38 monoclonal antibody (CD38MoAb) is labeled triple-class refractory (TCR) and recognized as a modern therapeutic challenge. In recent years, new agents have shown activity in this setting in single arm trials and many more are in development. The assessment of the impact of new agents in the TCR setting is impaired by limited information on the therapeutic outcomes of these patients when treated with non-investigational therapies. Methods: We utilized an independent RWD dataset of patients with MM refractory to CD38MoAb built from academic collaboration (MAMMOTH study) to identify patients with TCR MM who received subsequent therapy. Patients with MM that was CD38MoAb refractory but not TCR were followed longitudinally and only included if, after subsequent lines of therapy, MM became TCR. Patients were excluded if they had plasma cell leukemia, poor renal function (serum creatinine > 2 mg/dl) or if next therapy after TCR status included experimental agents. Refractoriness to a MM agent was defined as lack of objective response and/or progression while receiving therapy or within 60 days of last dose as per International Myeloma Working Group (IMWG) criteria. Time zero for analysis was initiation of next therapy after MM became TCR. Response to therapy and progression were adjudicated utilizing IMWG criteria. MM that was TCR but also exposed to 2 PIs, 2 IMiDs and a CD38MoAb was considered penta-exposed, and when refractory to 2 PIs, 2 IMiDs and a CD38MoAb, it was considered penta-refractory. Results: Data collection occurred between 2016 and 2018 (prior to commercial availability of selinexor, belantamab mafodotin, melflufen or ide-cel). Of 275 patients in the MAMMOTH study, 177 were included. Median age was 65 years (range 27-90), 53% were male, 76% white, 28 % had International Staging System stage 3, and 29% high risk cytogenetics [t(4;14), t(14;16) or del(17p)]. Median number of prior lines of therapy was 5 (range 3-17), median time from diagnosis to TCR was 4.8 years (range 0.9-19.4), 73% had received prior autologous hematopoietic cell transplantation, 84% were refractory to bortezomib or ixazomib, 58% were refractory to carfilzomib, 87% to lenalidomide and 74% to pomalidomide. Penta-exposed patients were 58% and penta-refractory 30%. The first regimen after MM became TCR included cytotoxic chemotherapy in 45%, CD38MoAb in 25%, SLAMF7 MoAb in 7%, carfilzomib in 24%, and pomalidomide in 34% of cases. Overall response rate (ORR) was 30% (20.9% PR, 7.3% VGPR and 1.7% CR). Median progression-free survival (PFS) was 2.8 mo. (95% C.I. 2.3-3.2) and overall survival (OS) 8.6 mo. (95% C.I. 6.8-10.3),(Figure). For Patients with MM that was penta-exposed (N=102), ORR was 30%, median PFS was 2.7 mo (95% C.I 2.1-3.2), and median OS 7.7 mo (95% C.I. 6.0-9.4). For patients with penta-refractory MM (N=53), ORR was 32%, median PFS was 2.5 mo. (95% C.I. 1.7-3.3), and median OS 6.9 mo. (95% C.I. 4.9-8.9). PFS or OS was similar among patients who were TCR but not penta-exposed, TCR plus penta-exposed but not penta- refractory or TCR plus penta-refractory (Figure). Outcomes according to next regimen after MM became TCR are outlined in the Table. In multivariable analysis, no factor was predictor of PFS while refractoriness to carfilzomib (HR 2.2, 95% C.I. 1.5-3.3), presence of high-risk chromosome abnormalities (HR 1.5, 95% C.I. 1.0-2.2) and short interval from diagnosis to TCR (HR 0.92, 95% C.I. 0.85-0.99) were predictors of shorter OS. Conclusions: Patients with TCR MM have a uniformly low chance of response to subsequent conventional therapy with short PFS and OS even when not penta-exposed or penta-refractory. This dataset provides efficacy benchmark for new therapies being developed in this setting. Figure 1 Figure 1. Disclosures Costa: BMS: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau. Cornell: Abvvie: Current Employment. Chhabra: GSK: Honoraria. Liedtke: Janssen Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Caelum: Membership on an entity's Board of Directors or advisory committees, Other: Clinical Trial Funding; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Kura Oncology: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Alnylam: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Kansagra: GSK: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Cota Health: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Alynylam: Membership on an entity's Board of Directors or advisory committees. Giri: PackHealth: Research Funding; CareVive: Honoraria, Research Funding. Malek: Amgen: Honoraria; Janssen: Other: Advisory board ; Cumberland Inc.: Research Funding; Bluespark Inc.: Research Funding; BMS: Honoraria, Research Funding; Sanofi: Other: Advisory Board; Medpacto Inc.: Research Funding; Takeda: Honoraria. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Usmani: Celgene/BMS: Consultancy, Research Funding, Speakers Bureau; Array BioPharma: Consultancy, Research Funding; Amgen: Consultancy, Research Funding, Speakers Bureau; Abbvie: Consultancy; EdoPharma: Consultancy; GSK: Consultancy, Research Funding; Janssen: Consultancy, Research Funding, Speakers Bureau; Sanofi: Consultancy, Research Funding, Speakers Bureau; Merck: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; SkylineDX: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Takeda: Consultancy, Research Funding, Speakers Bureau; Janssen Oncology: Consultancy, Research Funding; Bristol-Myers Squibb: Research Funding. Hari: Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; GSK: Consultancy, 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; Millenium: 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, Other, Research Funding, Speakers Bureau; Karyopharm: Consultancy; Celgene-BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau. Kumar: Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Novartis: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Antengene: Consultancy, Honoraria; Beigene: Consultancy; Oncopeptides: Consultancy; Sanofi: Research Funding.

Abstract Background Morbidity and mortality in tumor lysis syndrome (TLS) remains high despite increasing efforts for prevention, early detection and treatment in recent years. The current risk stratification system and treatment guidelines are largely consensus based without strong evidence. There is paucity of data on the in-hospital mortality and predictors of poor clinical outcome in this population. Methods We used the 2009-2011 Nationwide Inpatient Sample database to identify hospitalizations in patients ≥18 years with a diagnosis of TLS (International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM] code 277.88). Nationwide Inpatient Sample is the largest all-payer publicly available inpatient care database in the US. It contains data from five to eight million hospital stays from about 1,000 hospitals across the country and approximates a 20% sample of all US hospitals. The interval 2009-2011 was selected as ICD-9-CM code 277.88 for TLS was only introduced from the year 2009 onwards. Univariate and multivariate logistic regression were used to determine the independent predictors of in-hospital mortality. Data analysis was done using STATA version 13.0 (College Station, TX). Results Among the 997 admissions (mean age ± SD 67.58±3.33, 62.6 % males, and 80.4 % white) with TLS, in-hospital mortality was 14.44 %. Based on the results of univariate analyses (table 1), we used obesity, coronary artery disease, cardiac dysrhythmias, acute kidney injury and sepsis in the final regression model. We found that cardiac dysrhythmias (OR 4.79; 95% CI, 1.67-13.77; p=0.004) and sepsis (OR 19.70; 95% CI, 5.33-72.78; p<0.001) were independent predictors of increased in-hospital mortality in patients with TLS. Although certain myeloid and lymphoid malignancies are associated with higher risk of tumor lysis syndrome, we did not observe any statistically significant increased risk of mortality with one specific type of malignancy over the other. Similarly, none of the demographic characteristics (age <40 vs ≥40, sex or race), or hospital characteristics (rural vs urban, teaching vs non-teaching, small vs large bed-size or geographic region) predicted increased in-hospital mortality in these patients. Conclusion In this study of large national database, patients with TLS had an in-hospital mortality of 14.44 %. Independent predictors of in-hospital mortality were cardiac dysrhythmias and sepsis. Our findings might help physicians to identify sub-group of patients with TLS that are at increased risk of in-hospital mortality. Table 1 Baseline characteristics of patients with tumor lysis syndrome according to in-hospital survival Characteristics Survivors (n=853) Non-survivors (n=144) p Age( in years) Mean 68.40±3.06 67.58±3.33 0.73 Male sex 62.1 65.8 0.69 Race 0.16 White 81.52 73.54 Black 14.98 22.8 Hispanic 2.27 0 Others/unknown 1.22 3.66 Insurance status 0.41 Medicare 65.55 48.15 Medicaid 5.56 13.34 Private insurance 24.89 32.58 Self-Pay 0.65 2.99 No charge 0.63 0 Other 2.72 2.93 Region 0.13 Northeast 27.59 37.74 Midwest 28.23 12.92 South 31.34 39.9 West 12.84 9.44 Location/teaching status 0.59 Rural 7.87 10.16 Urban nonteaching 26.01 33.52 Urban teaching 66.12 56.32 Bed-size 0.84 Small 11.11 8.91 Medium 21.04 24.55 Large 67.85 66.54 Malignancies ALL 1.19 6.79 0.25 AML 2.34 6.99 0.29 CLL 27.86 16.83 0.18 CML 1.85 3.19 0.68 NHL 36.33 22.37 0.1 HD 0.53 0 0.32 Multiple Myeloma 7.39 6.56 0.82 Other hematologic malignancies 38.67 25.99 0.16 Solid tumors 35.87 26.8 0.32 Co-morbidities Smoking 15.89 21.87 0.46 Obesity 5.16 0 0.005 Dyslipidemia 27.8 16.86 0.18 Hypertension 54.17 49.19 0.63 Diabetes mellitus 27.81 16.65 0.15 PVD 1.71 3.19 0.65 CAD 13.78 3.19 0.02 AKI 60.06 79.54 0.03 CKD 28.81 25.23 0.65 Stroke 0.63 0 0.32 Sepsis 3.58 31.81 0.003 Cardiac dysrhythmias 17.96 43.42 0.04 Acute CHF 11.44 7.08 0.43 AKI=Acute Kidney Injury; ALL=Acute Lymphoblastic Leukemia; AML=Acute Myelogenous Leukemia; CAD=Coronary Artery Disease; CHF=Congestive Heart Failure; CKD=Chronic Kidney Disease; CLL=Chronic Lymphocytic Leukemia; CML=Chronic Myelogenous Leukemia; HD=Hodgkin Disease; NHL=Non Hodgkin Lymphoma; PVD=Peripheral Vascular Disease Disclosures No relevant conflicts of interest to declare.