Citations

Abstract The objective of this study was to analyze molecular changes that occur at the protein level in WM and IgM-MGUS in order to identify proteins dysregulated at disease progression and design new therapies. We employed antibody protein microarrays (BD Clonetech, CA) to measure changes in the patterns of protein expression between WM, IgM-MGUS and normal lymphoplasmacytic cells as control. The antibody array is a novel technique that assays protein differences directly by hybridizing fluorescently labeled protein mixtures from cell extracts onto a glass slide spotted with 512 different monoclonal antibodies specific for human proteins. CD 19+ and CD138+ purified lymphoplasmacytic cells were obtained from cryopreserved bone marrow samples of 5 newly diagnosed patients with WM and 5 patients with IgM-MGUS. Control plasma cells were obtained from 3 pooled CD19+ and CD138+ purified normal donor bone marrow cells. To assess differential expression, the mean of the ratios of Cy5/Cy3 for each sample were analyzed using the Clontech software to calculate an internally normalized ratio. The normalized data were analyzed by the Genespring software and changes of protein expression ≥2 fold in 60% of the samples as compared to normal control were identified in both WM and MGUS groups. There were 20 proteins upregulated in WM and 21 proteins in MGUS as compared to normal control cells These included proteins from the small GTPases such as the Ras, Rho and Ran families (Rab 4, MONA, RCC1, and CDC42GAP), Kinases/signaling proteins (ROK alpha, p62 dok, JAK, 14-3-3e), cyclin dependent kinase (CDK2), fatty acid synthase (HDAC3), NFkB pathway (IKKalpha, IKBe), and histone deacetylase. The WM and IgM MGUS differed in only 3 proteins using the 2-fold threshold. The WM samples showed overexpression of the heat shock protein Hsp90, the Ras family protein CDC25C, and the chemotaxis protein p43/EMAPII as compared to the MGUS samples. This is the first proteomic study of WM and MGUS patients. It identifies novel proteins dysregulated in WM that may serve in future clinical trials as targets for therapy such as Hsp90, NFkB, HDAC and fatty acid synthase. In addition, it identifies 3 proteins that identify disease progression from MGUS to WM, which could be useful for monitoring MGUS patients and designing targeted therapies to prevent progression to WM.

Abstract Abstract 3803 Background: The MMRC is a non-profit, disease-focused consortium founded in 2004 comprised of 13 North American centers with expertise in multiple myeloma (MM). The MMRC Inc. (Norwalk, CT), MMRC's member institutions, and several pharmaceutical partners work closely to speed early development of new treatment options for MM patients. In December 2007, MMRC headquarters staff implemented multiple project management (PM) business solutions to address trial barriers that delay activation of our phase I-II clinical trials and established trial metric benchmarks considered attainable at our member institutions. In November 2009, we reported initial data1 on trial activation: MMRC trials initiated between Sep08-Jul09 (n=5) demonstrated a 38% decrease in mean time to first patient dosed (FPFD), compared with the Early Group trials (EG) initiated before PM solutions (Jun06-Sept08; n=7 trials). These results also confirmed improvement over published metrics from Dilts et al 2,3. We present additional data on time to FPFD and now enrollment, to assess impact of the MMRC PM resources and processes on trial efficiency. Methods: Twenty-one (21) trials conducted within the MMRC from May 2006 to June 2010 had sufficient trial data for review. Data were collected by MMRC-funded project managers at the clinical centers using a web-based clinical trial management system (CTMS). FPFD was defined as the time from the member institutions' receipt of the final protocol (FP) from the trial sponsor, to the time the first patient was dosed on the trial at any participating MMRC member institution. With respect to enrollment, pre-study enrollment commitment (EC) established between MMRC and the study sponsor was defined as the total number of subjects committed to receive at least one dose of study drug across all participating MMRC centers on a trial; baseline enrollment timeline (BET) was prospectively defined as the target time period to attain EC. Results: Mean time to FPFD was 181 calendar days for the early group trials (EG, n=7) and 122 days for the recent group (RG) trials (n=14, Sep08 – Jun10), representing a 32% reduction of time to FPFD in the RG. Additionally, time from final protocol to first patient dosed at all MMRC centers on a trial decreased 18% from a mean of 7.7 months (EG) to 6.3 months (RG). With respect to MMRC trial enrollment, data was available for 16/21 trials (5 EG and 11 RG). 2 EG trials were missing data and 3 RG trials were still enrolling as of June 2010. The mean MMRC pre-study EC was 39 subjects per trial (n=16 trials; 626 enrollment target); the mean actual MMRC enrollment was 49 subjects per trial (n=16; 783 enrolled through Jun 29, 2010) representing a 25% increase in actual versus committed enrollment. Two trials did not meet MMRC EC: MMRC investigators discontinued their involvement in these trials at approximately 30% target EC due to trial complexity or low patient enrollment. 14/16 evaluable trials (88%) met their EC; 11/16 trials met EC within BET (69%) of which 8/16 trials (50%) reached EC 34% faster than their BET (representing a mean reduction of 4.5 months). The overall pre-study mean BET for 16 trials was 13.1 months. MMRC's actual mean enrollment timeline was 11.3 months for the group of 14 evaluable trials representing improvement over the original BET by a mean of 1.9 months (14%). We believe FP to FPFD is a more meaningful metric beyond that of first patient consented. Moreover, we believe that if all participating trial centers focus their efforts on dosing the first patient and within a targeted timeframe, it may improve our research efforts overall. Conclusion: Development of drugs in the clinical setting has become time and resource intensive. Activating and enrolling trials promptly is a priority for drug development. The MMRC's standardization of processes and support for site-based PM resources results in improved trial metrics. MMRC member centers met or exceeded pre-specified enrollment targets in 88% of the trials analyzed to date. Ongoing monitoring of trial conduct continues to reveal areas where increased focus is needed to realize further trial efficiencies. These trial metrics and measures to improve efficiency may be applied with similar expected benefit in all oncology disciplines. Disclosures: Wear: Multiple Myeloma Research Consortium (MMRC): Employment. Richardson:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Millennium: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Johnson & Johnson: Membership on an entity's Board of Directors or advisory committees. Revta:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Vij:Multiple Myeloma Research Consortium (MMRC): Research Funding; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Fiala:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Lonial:Bristo-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millenium Pharmaceuticals Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Onyx: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Francis:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Siegel:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Speakers Bureau; Millennium: Speakers Bureau. Schramm:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Jakubowiak:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC, Research Funding; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Speakers Bureau; Centocor Ortho-Biotech: Honoraria, Speakers Bureau; Exelixis: Honoraria, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Harvey:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Reece:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Honoraria, Research Funding; Ortho Biotech: Honoraria, Research Funding; Merck: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Facet: Research Funding; Otsuka: Honoraria, Research Funding. Gul:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Jagannath:Celgene: Honoraria; Millenium: Honoraria; Orthobiotec: Honoraria; Onyx Pharma: Honoraria; Merck: Honoraria; Proteolix: Honoraria; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. La:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Hofmeister:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Jansak:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Stewart:Millennium: Consultancy; Celgene: Honoraria; Multiple Myeloma Research Consortium: Member Institution of the MMRC. Hagerty:Mayo Clinic: Employment; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Wolf:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Speakers Bureau; Millennium: Speakers Bureau; Novartis: Speakers Bureau; Orthobiotech: Speakers Bureau. Davis:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Krishnan:Celgene: Speakers Bureau; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Duarte:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Zimmerman:Millennium, Celgene: Speakers Bureau. Cisneros:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Kumar:Celgene: Consultancy, Research Funding; Millennium: Research Funding; Merck: Consultancy, Research Funding; Novartis: Research Funding; Genzyme: Consultancy, Research Funding; Cephalon: Research Funding; Bayer: Research Funding; Multiple Myeloma Research Consortium: Member Institution of the MMRC. Birgin:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Ott:Multiple Myeloma Research Consortium (MMRC): Employment. Tasca:Multiple Myeloma Research Consortium (MMRC): Employment. Kelley:Multiple Myeloma Research Consortium (MMRC): Employment. Anderson:Millennium: Consultancy; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Consultancy; Novartis: Consultancy; Onyx: Consultancy; Merck: Consultancy; Bristol-Myers Squibb: Consultancy; Acetylon: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Giusti:Multiple Myeloma Research Consortium (MMRC): Employment.

Abstract Background: Persons of African ancestry (AA) have a 2-3-fold higher risk of multiple myeloma (MM) than persons of European ancestry (EA). Like other B-cell malignancies, genome-wide association scans (GWAS) have identified MM risk variants in the HLA region in persons of EA. We conducted a case-control analysis with data from the National Marrow Donor Program (NMDP)1comprising MM patients typed for bone marrow transplant to donor controls matched by race-ethnicity, and found associations between specific HLA alleles/haplotypes and MM risk that varied by race and ethnicity. To confirm our results and identify additional novel signals, we have now investigated associations between HLA alleles and haplotypes and MM risk in the African American Multiple Myeloma Study (AAMMS) Cohort. Methods: The source of subjects was the AAMMS, in which AA MM patients were identified from 10 cancer centers and 4 Surveillance, Epidemiology and End-Results (SEER) Program cancer registries in order to identify genetic risk factors for MM among AAs. A GWAS was conducted using the Illumina Human Core BeadChip array on DNA samples from 1,305 AA MM patients in the AAMMS comparing results to those from 7,078 AA controls with GWAS data generated from the Illumina 1MDuo2. The major histocompatibility complex (MHC) region single nucleotide polymorphisms (SNPs) were imputed to classical HLA variants using HIBAG. Unconditional logistic regression was used to estimate HLA associations, adjusting for sex, age and the first 2 principal components. P-values were adjusted for false discovery rate (FDR) for each locus group. Results: We did not identify any single HLA alleles associated with MM risk among AAs. However, several B*07:02-containing haplotypes were associated with MM risk (odds ratios [OR] ranging from 2.38 to 2.64 and FDR P-values ranging from 1.43 x 10-6 to 3.57 x 10-8). We found associations between MM risk and genotypes containing DRB3*02:02, including DRB3*02:02~DRB1*11:01+ DRB3*02:02~DRB1*11:01 (OR=1.93, PFDR= 9.36 x 10-5) similar to those observed in the NMDP study1. Novel findings included associations between MM risk and HLA Class I haplotypes B*53:01+ B*57:01 (OR=1.94, PFDR= 0.003) and C04:01~B*53:01+C*06:02~B*57:01 (OR=1.96, PFDR= 0.0050). Results from an ongoing meta-analysis between the two data sets (one based on an imputed GWAS and one based on NMDP HLA typing) will be presented. Conclusions: This study is the second to examine HLA alleles and risk of MM among AA's and is by far the largest. We confirmed a previously observed association between an HLA Class II DRB3 variant and MM risk and confirmed an association with B*07 haplotypes previously observed among EAs1. We also identified novel associations between other HLA Class I haplotypes and MM risk in AA's. Because HLA is highly polymorphic, many HLA alleles are rare variants for which genetic associations are difficult to detect without very large sample sizes. Further investigation with large sample sizes will be necessary to refine these associations in order to better identify the underlying causal alleles and determine the functional significance of these HLA associations. 1Beksac M, Gragert L, Fingerson S, et al.: HLA polymorphism and risk of multiple myeloma.Leukemia. 2016 Jul 27. doi: 10.1038/leu.2016.199. 2Rand KA, Song C, Hwang AE, et al. Genetic susceptibility markers of multiple myeloma in African-Americans. Abstract # 2030, 56th Annual American Society of Hematology Meeting, San Francisco, California, 2014. Disclosures Ailawadhi: Pharmacyclics: Consultancy; Novartis: Consultancy; Amgen Inc: Consultancy; Takeda Oncology: Consultancy. Nooka:Spectrum, Novartis, Onyx pharmaceuticals: Consultancy. Zonder:Pharmacyclics: Other: DSMC membership; Prothena: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Lonial:BMS: Consultancy; Novartis: Consultancy; Millenium: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Merck: Consultancy; Celgene: Consultancy; BMS: Consultancy; Novartis: Consultancy; Onyx: Consultancy; Janssen: Consultancy; Onyx: Consultancy.

Abstract Background: Polycythemia vera (PV) is characterized by erythrocytosis, thrombocytosis, and/or leukocytosis and a broad range of disease-related symptoms. In high-risk patients, the most common first-line treatment is hydroxyurea (HU). The open-label RESPONSE trial demonstrated that ruxolitinib (RUX), a JAK1/JAK2 inhibitor, provided superior efficacy compared with best available therapy in patients with PV who were resistant to or intolerant of HU according to modified European LeukemiaNet (ELN) criteria. This study (RELIEF) was conducted in patients receiving a stable dose of HU and who were generally well controlled but reporting disease-associated symptoms, comparing the change in PV-related symptom burden in patients continuing their HU therapy with those switching to RUX treatment. Methods: RELIEF was a randomized, multicenter, double-blind, double-dummy, phase 3b study of patients with PV aged ≥18 years on a stable dose of HU monotherapy and reporting PV-related symptoms. Patients were required to be receiving HU for ≥12 weeks prior to enrollment and on the same dose level for the last 4 weeks, and have a score ≥8 on the Myeloproliferative Neoplasm Symptom Assessment Form (MPN-SAF) cytokine total symptom score (TSS-C). The TSS-C comprised symptoms of itching, tiredness, muscle ache, night sweats, and sweats while awake; each symptom was rated on a scale of 0=absent to 10=worst imaginable, with a maximum TSS-C score of 50. Patients also had to meet one of the following: ≤1 phlebotomy in the previous 6 months or no palpable splenomegaly. Those eligible were randomized 1:1 to receive RUX 10 mg BID and HU-placebo, or HU at the same dose/schedule and RUX-placebo. Dose adjustments were permitted for safety and efficacy. After Week 16, patients could receive open-label RUX until Week 48. The primary endpoint was the proportion of patients with a ≥50% reduction in TSS-C at Week 16; secondary endpoints included proportion of patients with a ≥50% reduction in individual TSS-C symptoms and safety. Results: Overall, 54 and 56 patients were randomized to RUX and HU, respectively; 87.0% and 89.3% remained on treatment through Week 16. At baseline, the median age (range) was 64 (36-87) in the RUX group and 66 (19-85) in the HU group; 44% and 61% were men. The majority of patients in the RUX and HU groups did not have baseline platelet counts or WBC above ELN thresholds: platelets >400 and ≤600 x 109/L (RUX 31.5%, HU 28.6%), >600 x 109/L (3.7%, 8.9%); WBC >10 and ≤15 x 109/L (16.7%, 16.1%), >15 x 109/L (11.1%, 14.3%). In the RUX and HU groups, the mean TSS-C at screening (22.4, 23.1) was higher than that at baseline (16.7, 18.0); the ratio of screening to baseline TSS-C was 1.7 and 1.6. The proportion of patients achieving a ≥50% reduction from baseline in TSS-C at Week 16 (primary endpoint) was 43.4% in the RUX group and 29.6% in the HU group (P=0.139; OR, 1.82; 95% CI, 0.82-4.04). The proportions of patients in the RUX vs HU groups achieving a ≥50% reduction in scores for itching and tiredness at Week 16 were 40.0% vs 26.4% and 54.2% vs 32.0%, respectively. Median percentage changes in individual TSS-C symptoms are shown in Table 1. Additional analyses found no correlation between individual changes in HU dose from baseline to Weeks 13-16 and percentage change in TSS-C in the HU arm (r2=0.030). Even patients maintaining the same HU dose from prior to study entry through Week 16 reported symptom improvement: 12/35 (34.3%) with no dose change, 4/12 (33.3%) with a dose increase, and 0/9 (0%) with a dose decrease had a ≥50% reduction in TSS-C. The most common nonhematologic adverse events in the RUX arm on randomized treatment were fatigue (20.4% RUX vs 10.7% HU), headache (16.7% vs 5.4%), and dizziness (13.0% vs 8.9%). The most common adverse events on HU were diarrhea (9.3% RUX vs 19.6% HU) and constipation (7.4% vs 12.5%); most events were grade 1 or 2. Grade 3 or 4 anemia or thrombocytopenia (lab values) were not reported in the RUX group; two patients in the RUX group had grade 3 or 4 neutropenia. Conclusion: In generally well controlled PV patients receiving a stable dose of HU, there was a positive trend in symptom improvement for patients switched to RUX therapy versus those continuing on HU therapy, although this was not statistically significant. The 34% response rate among patients who continued to receive a stable HU dose suggests a placebo effect that led to an underpowered study. Further analyses are required to better interpret these findings. Disclosures Off Label Use: Ruxolitinib is a JAK1/JAK2 inhibitor approved for the treatment of patients with intermediate or high-risk myelofibrosis, including primary myelofibrosis, post polycythemia vera myelofibrosis, and post-essential thrombocythemia myelofibrosis. Vannucchi:Novartis Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Yacoub:Alexion Pharmaceutical: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Sanofi Aventis: Membership on an entity's Board of Directors or advisory committees. Koschmieder:Novartis Pharmaceuticals: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Byrne:Novartis Pharmaceuticals: Honoraria. Verstovsek:Incyte Corporation: Research Funding. Hunter:Incyte Corporation: Employment, Equity Ownership. Jones:Incyte Corporation: Employment, Equity Ownership. He:Incyte Corporation: Employment, Equity Ownership. Morozov:Novartis Pharmaceuticals: Employment, Equity Ownership.

Abstract Multiple myeloma (MM) is 2-3 times more common among African-Americans compared to non-Hispanic whites. The 2-3-fold increased risk among family members of cases suggests a genetic contribution to risk. Genome-wide association studies (GWAS) in populations of European ancestry have identified seven novel risk loci at 2p23.3 (rs6746082), 3q26.2 (rs10936599), 3p22.1 (rs1052501), 6p21.32 (rs2285803), 7p15.3 (rs4487645), 17p11.2 (rs4273077) and 22q13.1 (rs877529) (Broderick, et al. Nat Genet, 2011, Chubb, et al. Nat Genet, 2013), three of which were replicated in another European series (Martino et al., Br J Haematol, 2012). Here we examined the index signals and conducted fine-mapping for each locus in a case-control study of 1,049 multiple myeloma cases and 7,084 controls of African ancestry to identify better markers of risk and novel independent loci in seven previously reported regions in this high risk population. Incident cases were recruited from 10 clinical centers and SEER cancer registries from 2011 to 2013 and genotyped using the Illumina HumanCore GWAS array. Control data were obtained from previous genome-wide studies of breast and prostate cancer, genotyped using the Illumina 1M-Duo in 4425 male controls from the African Ancestry Prostate Cancer Consortium (consisting of 14 independent studies) and 2632 female controls from a breast cancer GWAS of African-American women (consisting of 9 independent studies). Imputation to 1000 Genomes (March 2012 release) was conducted for regions around six of the previously identified single nucleotide polymorphisms [SNPs] (the HLA region harboring rs2285803 is still being imputed, results will be presented). A case-control analysis of SNPs/indels >1% frequency within 250 kb of each index variant was conducted using unconditional multivariable logistic regression adjusting for age, sex and five leading principal components. Region-specific alpha levels were determined through permutation tests. The minimum alpha level across the six regions was α=0.002. All previously reported risk variants were common in African-Americans (minor allele frequency [MAF] >0.05). For five of the six SNPs, we had ≥94% power to detect the same effect observed in non-Hispanic whites, and 64% power for the less common variant rs10936599 (MAF=0.07). We observed directionally consistent effects (odds ratio [OR]>1) for the six risk variants tested, with three replicating at p≤0.05 (7p15.3, p=1.4x10-7; 17p11.2, p=0.05; 22q13.1, p=0.02). For three of the six regions, we observed better markers of risk in African-Americans that were correlated with the index SNP in Europeans (7p15.3, rs56333627, p=1.5x10-5, r2=0.89; 17p11.2, rs34562254, p=2.9x10-3, r2=0.90; 22q13.1, rs2092410, p=1.1x10-4 r2=.71). The missense variant identified in the 17p11.2 region (rs34562254, Pro251Leu) is located in TNFRSF13B, which encodes the protein TACI, a B cell surface receptor which plays a role in B cell maturation, apoptosis and antibody production by inducing activation of transcription factors including NFAT and NFκβ. In addition, there is evidence suggesting that TACI is involved in MM pathogenesis. Our results demonstrate that many of the risk loci for MM found in European ancestry populations are also risk loci in men and women of African ancestry and that by fine-mapping, we are able to identify variants that better capture risk in populations of African ancestry. Disclosures Terebelo: Celgene Corp: Membership on an entity's Board of Directors or advisory committees. Lonial:Millennium: The Takeda Oncology Company: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Onyx Pharmaceuticals: Consultancy, Research Funding.

INTRODUCTION: Ileus is a known early complication following kidney transplant (KTx). There is limited data largely from case reports documenting the occurrence of ileus in KTx patients. Potential risk factors include post-operative setting, steroid withdrawal, or even peritoneal sclerosis in patients with a history of peritoneal dialysis (PD). The aim of this study is to assess inpatient prevalence and outcomes of ileus in the same admission as the KTx occurred. METHODS: Case-control study using the NIS 2012-2016. The ICD9-10CM procedural codes for KTx were used to identify all patients that underwent KTx during the same admission. None were excluded. Patients with associated ileus were identified using ICD9-10CM diagnostic codes. The primary outcome was determining the odds of ileus in post-KTx patients. Secondary outcomes were inpatient mortality, postoperative complications, resource utilization, length of hospital stay (LOS), total hospitalization charges and costs. Propensity score matching was used to create a matching population for gender, age and Charlson Comorbidity Index. Multivariate analysis were used to adjust for income in patients’ zip code, hospital region, location, size and teaching status. RESULTS: A total of 89,065 KTx patients were identified, of which 16,880 were propensity score matched for the selected covariates. The mean patient age was 50 years, and 40% were female. On multivariate analysis, the odds of ileus in the KTx cohort was 3.90 ( P < 0.01) compared to patients who did not undergo KTx surgery. When patients that underwent KTx and developed ileus during the admission, they had greater adjusted odds of shock, TPN, AKI, multi-organ failure, post-op infection, Deep Vein Thrombosis, Pulmonary Embolism, and bleeding compared to patients with no KTx. In addition, those who developed ileus had greater hospitals costs, charges, and length of stay (LOS) (Table 1). CONCLUSION: Patients undergoing kidney transplant are more likely to develop ileus than patients who did not have kidney transplant surgery, confirming known reports documenting ileus in the early post-operative period. This cohort of patients who developed ileus following transplant have greater odds of post-operative complications, as well as increased morbidity, resource utilization and economic burden. Future studies should focus on elucidating the etiology of ileus following kidney transplant, as well as associated factors, and creating strategies to address it.

INTRODUCTION: Patients with Celiac disease (CD) are at increased risk of infections, as evidenced by increased prevalence of tuberculosis, influenza, and sepsis. Alterations in immunity and microbiota can contribute to this predisposition. Although the prevalence of Clostridium difficile infection (CDI) continues to increase in the US, there is little data on the association and outcomes in patients with CD. The aim of this study was to assess inpatient prevalence, outcomes and resource utilization of patients with CD and CDI in the US in the past decade. METHODS: Retrospective cohort study using the NIS 2007 to 2016. All patients with CD were included using ICD9-10CM codes. Cohorts were stratified for CDI coexistence. None were excluded. The primary outcome was determining the association of CD with CDI. Secondary outcomes were determining the CDI inpatient prevalence trend in the CD patients, as well as mortality, morbidity, length of hospital stay (LOS), total hospital charges and costs, which were adjusted for inflation using the Consumer Price Index. Multivariate regression was used to adjust for age, gender, income in patient zip code, Charlson Comorbidity Index, hospital region, location, size and teaching status. RESULTS: A total of 337,201 patients with CD were identified, of which 5,500 had associated CDI. Mean age was 55 years and 71% were female. The inpatient prevalence of CDI in patients with CD increased from 0.9/100,000 admissions in 2007 to 1.65/100,000 admissions in 2016. Patients with CD displayed increased adjusted odds (aOR:1.56, P < 0.01) of coexisting CDI compared to patients without CD. Patients with CD had lower odds of mortality, ICU, shock, multiorgan failure, hospital costs, charges, and LOS but increased odds of requiring TPN compared to the general population admitted population with CDI. CONCLUSION: Patients with CD had higher odds of CDI compared to non-CD patients, which could be attributed to CD-related gut mucosal integrity, immunological or microbiome alterations. The inpatient prevalence of CDI in patients with CD increased from 2007 to 2016. This may be a reflection of the general increase of CDI cases in the inpatient population and with improved CD diagnostic modalities. Patients with CD and CDI did show decreased morbidity/mortality and resource utilization, suggesting that the clinical course is less severe. Further studies should further assess the gut immunological and microbiome changes in CD to clarify potential factors associated with CDI.

INTRODUCTION: Gender in ulcerative colitis (UC) is a well-studied topic with contradicting results with a slight male predominance seen in large population based studies in Olmsted County, Minnesota, while other studies have shown a slight female predominance. Yet, the relationship between UC and gender is not well-understood. Hence, it is unclear what is responsible for differences in gender. The aim of this study is to assess the inpatient prevalence and inpatients outcomes of UC by gender. METHODS: Case-control study using the NIS 2016, the largest public inpatient database in the US. All patients with ICD9-10CM codes for UC were included. None were excluded. Female patients were identified within the database using the female gender indicator. The primary outcome was determining the odds of UC in female patients compared to males. Secondary outcomes included determining inpatient morbidity, mortality, resource utilization, colectomy rates, hospital length of stay (LOS), and inflation-adjusted total hospital costs and charges. Propensity score matching was used to create a matching population for Charlson Comorbidity Index and age. Multivariate regression analyses were used to adjust for income in patient zip code, hospital region, location, size and teaching status. RESULTS: A total of 38, 950 patients with UC were identified and propensity matched for selected covariates. The mean patient age was 45, and 47.2% were female. For the primary outcome, females had decreased adjusted odds (aOR: 0.92, P < 0.01) of being admitted for UC. For secondary outcomes, female patients displayed lower adjusted odds of multi-organ failure, being placed on TPN, and undergoing colectomy. Females also had lower adjusted mean hospital costs, charges, and length of stay (LOS). All adjusted odds and means are displayed in Table 1. CONCLUSION: Females have lower inpatient prevalence of UC, complications, and economic burden compared to males. This reinforces the slight male predominance that was seen in the older Olmstead County population-based studies. One can speculate that the difference in complications, costs, charges, and LOS indicate that females have less severe disease at hospital admission. Numerous studies have examined the effect of gender on disease severity and outcomes, and there continues to be conflicting data. Thus, future studies are needed to determine what contributes to the variation in disease prevalence and course by gender, and possibly better tailor treatment regimens by gender.

INTRODUCTION: Esophageal food impaction is a commonly encountered clinical scenario that may warrant emergent intervention, but epidemiological studies are lacking. No major studies have examined the inpatient prevalence of food impaction during the weekend. Therefore, the aim of our study is to assess the inpatient prevalence of food impaction during the weekend compared to week days. METHODS: Case-control study using the NIS 2016, the largest public inpatient database in the US. All patients with ICD10CM codes for food bolus impactions were included. None were excluded. The primary outcome was determining the odds of food bolus impaction during the weekend days as compared to the weekdays. Secondary outcomes included determining rates of esophageal perforation, hospital costs, charges and length of stay (LOS) when comparing admissions during a weekend to admissions during a weekday. Multivariate regression analyses were used to adjust for age, gender, Charlson Comorbidity Index, income in patient zip code, hospital region, location, size and teaching status. RESULTS: 7,025 patients with food bolus impaction were identified. 2,475 underwent emergent EGD (within 24 hours) and 1,935 were admitted during the weekend. Mean patient age was 66.7 years (47.9% were female). Primary outcome: after adjusting for confounders, patients admitted for food impaction displayed significantly higher odds of admission during the weekend days (aOR:1.5, P < 0.01) when compared to the weekdays. No significant difference in esophageal perforation was observed between the two cohorts. Patients admitted during the weekend displayed lower hospitalization costs compared to patients admitted during the weekdays (aMean: -$1,756, P = 0.02), but no differences in hospitalization charges or length of stay were observed. All outcomes are displayed in Table 1. CONCLUSION: The odds of being admitted for esophageal food impaction are higher on weekends than weekdays. There are no different odds of esophageal perforation. This may be reflecting the previous studies demonstrating increased odds of food impaction during leisure times, as weekends in the US are often reserved for leisure activities rather than work. Interestingly, patients admitted during the weekend actually have lower adjusted mean hospital costs. This may suggest that these patients are likely admitted for observation or stay longer periods of time in the ED and hence do not generate the same hospital bill as an admission.