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AbstractBackgroundAn analysis of the ethnoracial and educational composition of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, and the relationship between ethnoracial/education and screening, enrollment, dropout, and biomarkers is needed to assess the generalizability of ADNI data to diverse populations, and to inform efforts to increase diversity.MethodData from all 4 ADNI phases were used to determine ethnoracial and educational breakdown and differences of the following ADNI participation metrics: screening, screen fails, enrollment, as well as drop‐out within and between ADNI phases. Multivariable logistic regression was used to analyze the association between ethnoracial and educational group and either amyloid positivity or ApoE e4 status, adjusting for age, gender, and diagnostic group.ResultAcross the 4 ADNI phases (screened: n=3,739; enrolled: n=2,286), 11% of screened and enrolled participants identified as Latinx, non‐Latinx Black, or non‐Latinx‐Asian and 16% of the screened and 15% of the enrolled participants reported having =<12 years of education. In contrast, the 2019 US Census American Community Survey for adults 60+ reports 25% non‐White, and 44 % =< 12 years of education. There were no observed differences between participants from historically underrepresented ethnoracial groups and NLW participants in screen fail and dropout rates. We found that individuals with =<12 years of education failed screening at a significantly higher rate (p=.01) and rolled‐over significantly less to subsequent ADNI phases (p<.001) compared to individuals with an education >12 years. Identifying as non‐Latinx Asian was associated with lower odds of being amyloid positive (OR=0.36; p=.014) and an ApoE4 carrier (OR=0.36; p=.008) compared to NLW. Identifying as Latinx was associated with being less likely to be amyloid positive than NLW participants (OR=0.54; p=.031).ConclusionCompared to the US Census, ADNI underrepresents enrollment of Latinx, non‐Latinx Black, and non‐Latinx Asian participants and those with low educational attainment. Hence, ADNI reflects the on‐going challenges with recruiting and enrolling underrepresented populations into most AD research. Our findings highlight the need for ADNI to increase enrollment and engagement of underrepresented ethnoracial and educational populations. ADNI is actively undertaking steps to implement improved recruitment approaches aimed at increasing ethnoracial and socioeconomical representation in multi‐center cohort studies.

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

ABSTRACT Background The COVID-19 pandemic displaced newly matched emergency medicine “pre-interns” from in-person educational experiences at the end of medical school. This called for novel remote teaching modalities. Objective This study assesses effectiveness of a multisite Accreditation Council for Graduate Medical Education (ACGME) sub-competency-based curricular implementation on Slack during the first wave of the COVID-19 pandemic in the United States. Methods Emergency medicine residency programs were recruited via national organization listservs. Programs designated instructors to manage communications and teaching for the senior medical students who had matched to their programs (pre-interns) in spring/summer 2020. Pre- and post-surveys of trainees and instructors assessed perceived preparedness for residency, perceived effectiveness of common virtual educational modalities, and concern for the pandemic's effects on medical education utilizing a Likert scale of 1 (very unconcerned) to 5 (very concerned). Data were analyzed using descriptive statistics and the t test. Results Of 276 possible residency programs, 28 enrolled. Of 324 possible pre-interns, 297 (91.7%) completed pre-surveys in April/May and 249 (76.9%) completed post-surveys in June/July. The median weeks since performing a physical examination was 8 (IQR 7–12), since attending in-person didactics was 10 (IQR 8–15) and of rotation displacement was 4 (IQR 2–6). Perceived preparedness increased both overall and for 14 of 21 ACGME Milestone topics taught. Instructors reported higher mean concern (4.32, 95% CI 4.23–4.41) than pre-interns (2.88, 95% CI 2.74–3.02) regarding the pandemic's negative effects on medical education. Conclusions Pre-interns reported improvements in residency preparedness after participating in this ACGME sub-competency-based curriculum on Slack.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Abstract Meta-analysis based on individual participant data (IPD) is a powerful methodology for synthesizing evidence by combining information drawn from multiple trials. Hitherto, its principal application has been in questions of clinical management, but an increasingly important use is in clarifying trials methodology, for instance in the selection of endpoints, as discussed in this review. In oncology, the Aide et Recherche en Cancérologie Digestive (ARCAD) Metastatic Colorectal Cancer Database is a leader in the use of IPD-based meta-analysis in methodological research. The ARCAD database contains IPD from more than 38 000 patients enrolled in 46 studies and continues to collect phase III trial data. Here, we review the principal findings of the ARCAD project in respect of endpoint selection and examine their implications for cancer trials. Analysis of the database has confirmed that progression-free survival (PFS) is no longer a valid surrogate endpoint predictive of overall survival in the first-line treatment of colorectal cancer. Nonetheless, PFS remains an endpoint of choice for most first-line trials in metastatic colorectal cancer and other solid tumors. Only substantial PFS effects are likely to translate into clinically meaningful benefits, and accordingly, we advocate an oncology research model designed to identify highly effective treatments in carefully defined patient groups. We also review the use of the ARCAD database in assessing clinical response including novel response metrics and prognostic markers. These studies demonstrate the value of IPD as a tool for methodological studies and provide a reference point for the expansion of this approach within clinical cancer research.

AbstractThromboses are prevalent in POEMS syndrome, but few risk factors for POEMS‐associated thrombosis have been identified. The objective of this study is to identify novel risk factors for POEMS‐associated thrombosis. In this retrospective cohort of 230 POEMS patients, 27% developed thrombosis. Arterial events were slightly more common than venous. Stroke accounted for 26% of all thromboses and 53% of arterial events. There were differences in baseline features between the thrombosis group and the no thrombosis group, and these were driven by patients with arterial thrombosis. Risk factors for arterial thrombosis included thrombocytosis, elevated hemoglobin/hematocrit, extravascular volume overload, and splenomegaly. Hyperprolactinemia appeared to be a risk factor for venous thrombosis. The risk of thrombosis was most striking among men with elevated hemoglobin (32% vs. 5%, p < .001) and hematocrit (42% vs. 5%, p < .001) compared to men without. Most thromboses occurred prior to POEMS directed therapy, and most that occurred during therapy happened within 3 months of diagnosis. Twenty‐one percent of patients with thrombosis had recurrence. In recognition of high overall rates of thrombosis in this population, all patients with POEMS syndrome should receive prophylactic antiplatelet therapy, and clinicians should consider anticoagulation in patients with risk factors for POEMS‐associated thrombosis.

Abstract Background: There has been a steady improvement in the survival of patients with multiple myeloma (MM) due to better understanding of the pathophysiology, use of novel agents, and risk-adapted therapy. These improvements were largely observed in clinical trials and tertiary center populations and questions remain about the generalizability of these findings to patients in community settings. Here, we compared the experience of patients seen at Mayo Clinic, Rochester (MAYO) and those followed in the Surveillance, Epidemiology, and End Results Program (SEER), to gain a better understanding of current trends in survival. Methods: We studied all patients with MM diagnosed between 01/2004 and 12/2018 seen at MAYO and those followed in SEER (18 registry research data 2000-2018, 11/2020 submission). For comparison with SEER, MAYO patients who were alive at the end of SEER follow-up (12/31/2018) were censored. For the estimation of relative survival, patients alive at the end of 2019 were censored to align with the published United States general population rate tables. Overall survival (OS) estimates were calculated using the method described by Kaplan and Meier. Multivariable-adjusted proportional hazards regression models were used to assess the association between the 5-year interval of diagnosis and overall survival. Expected OS estimates were calculated based on United States general population rate tables from the Human Mortality Database using a conditional approach. Standardized mortality ratios (SMR) were calculated by dividing the number of observed deaths by the number of expected deaths in age- and sex-matched general United States population controls. Linear regression models were fit to test for trends in SMRs over time. Results: The median age at diagnosis was 3 years lower in patients seen at MAYO compared to patients followed in SEER. The proportion of patients 75 years and older at the time of diagnosis was higher in SEER (26.3%) compared to MAYO (15.3%). Median OS was 6.7 years (95% CI 6.3-7.1) for MAYO compared to 4.6 years (95% CI 4.5-4.7) in SEER (Figure 1). When stratifying OS by year of diagnosis (in 5-year-intervals: 2004-2008, 2009-2013, 2014-2018), improvements were seen in both patient populations over time, albeit to a greater extent in patients seen at MAYO (Figures 2 and 3). The results were consistent after adjusting for age and sex in both MAYO and SEER. There were improvements in OS across all three 5-year intervals and age groups in both patient populations. In MAYO, these improvements were most pronounced during the second 5-year interval for younger patients (&lt; 65 years) and more pronounced during the last 5-year interval for older patients (&gt; 65 years). In SEER, the improvements were more gradual across the three 5-year intervals, but it was noted that the relative improvements in younger patients were greater than in older patients during the last 5-year interval. Early mortality decreased over time in both patient populations: from 11% to 6% at MAYO and from 23% to 16% in SEER (Figure 4). To examine the observed improvements in the context of the survival trends of the general population, we stratified the SMRs by patient population, age at diagnosis, and year of diagnosis, and a lack of improvement in excess mortality was observed among older patients (&gt;75 years) in both populations over time (Figure 5). Three- and five-year overall survival estimates were higher in patients seen at MAYO compared to patients followed in SEER, but the survival advantage diminished with increasing follow-up time (Figure 6). On further sub-group analysis within MAYO patients, excess mortality was observed for all International staging system (ISS) stages and patients with high-risk (HR) cytogenetics. Stratifying the SMRs by age and risk group, decreases in excess mortality were most pronounced among older patients, particularly those with indolent disease (Figure 7). Conclusions: Survival outcomes in MM continued to improve in both patient populations. These improvements were more pronounced in MAYO compared to SEER, which may be partially explained by differences in access to specialized care. The most pronounced improvements were observed among younger patients and those with favorable disease characteristics. Future efforts need to focus on improving therapies for patients with unfavorable disease characteristics and on increasing access to specialized care in community settings. Figure 1 Figure 1. Disclosures Kapoor: Glaxo SmithKline: Research Funding; Takeda: Research Funding; Sanofi: Research Funding; Amgen: Research Funding; Sanofi: Consultancy; Pharmacyclics: Consultancy; Ichnos Sciences: Research Funding; BeiGene: Consultancy; Regeneron Pharmaceuticals: Research Funding; AbbVie: Research Funding; Karyopharm: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy. Dingli: Alexion: Consultancy; Apellis: Consultancy; Janssen: Consultancy; Novartis: Research Funding; GSK: Consultancy; Sanofi: Consultancy. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Lin: Novartis: Consultancy; Celgene: Consultancy, Research Funding; Juno: Consultancy; Takeda: Research Funding; Gamida Cell: Consultancy; Vineti: Consultancy; Merck: Research Funding; Bluebird Bio: Consultancy, Research Funding; Legend: Consultancy; Sorrento: Consultancy; Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding. Dispenzieri: Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding; Takeda: Research Funding; Oncopeptides: Consultancy; Alnylam: Research Funding; Pfizer: Research Funding. Kumar: Tenebio: Research Funding; Amgen: Consultancy, Research Funding; Oncopeptides: Consultancy; Bluebird Bio: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; Takeda: 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; Antengene: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract INTRODUCTION: Belantamab mafodotin (BLMF) is a first-in-class, antibody-drug conjugate targeting B-cell maturation antigen on myeloma cells. BLMF was approved for use in patients (pts) with advanced multiple myeloma (MM). Due to the risk of ocular toxicity, it is made available through a restricted program under a Risk Evaluation and Mitigation Strategy. It is speculated that monomethyl auristatin F (MMAF), the cytotoxic payload of BLMF may be internalised by the corneal epithelium via an off-target mechanism, leading to epithelial changes. In the pivotal DREAMM-2 trial, only 1% of pts permanently discontinued treatment due to keratopathy. Data regarding BLMF-induced ocular toxicity following its commercial availability since August 2020, are sparse, and in this context, we evaluated our clinical experience with BLMF to determine the impact of its ocular toxicity on outcomes when utilized outside of stringent clinical trial settings. METHODS: We reviewed records of pts with advanced MM who were evaluated at Mayo Clinic between 09/01/2020-07/01/2021 and initiated on BLMF monotherapy following a baseline ophthalmic examination that was also performed prior to each dose, and as needed for worsening ocular symptoms. Data pertaining to ocular symptoms, best corrected visual acuity (BCVA) and corneal examinations were collected and analyzed with respect to patient outcomes. Adverse events were graded using the National Cancer Institute Common Toxicity Criteria for Adverse Events v5.0. RESULTS: Thirty-four pts, with median age of 67 (range:49-90) years who received BLMF at the approved 2.5 mg/kg dose as their first infusion and completed at least one cycle were included. All patients were instructed to use preservative-free lubricant eye drops at least 4 times a day, starting with the initial infusion and continuing until the end of therapy, and avoid contact lenses. The median number of prior lines of therapy was 8 (range: 2-15). The median time to BLMF initiation from the diagnosis of MM was 7.5 (range: 1.3-19.7) years. The median follow-up from initiation of BLMF was 7 [95% confidence interval (CI): 5.7-8.6] months, and the median number of BLMF doses administered was 2 (range: 1-6). Twenty-seven (79%) pts experienced ocular toxicity, comprising keratopathy [n=25 (74%)] and/or decreased visual acuity [n=21 (62%)]. Table 1 outlines the keratopathy-related details. Thirteen pts (38%) developed symptomatic xerophthalmia. A clinically significant decrease in BCVA of equal or worse than 20/40 in the better-seeing eye was observed in 12 (35%) pts at a median of 1.7 (95%CI: 1.4-2.2) months of commencing BLMF. In 7 (58%) pts, it improved to better than 20/40 within a median of 3 (range: 0.7-4) months from the time of documentation of impaired BCVA following withholding treatment (n=5), with or without subsequent dose reduction or its permanent discontinuation (n=2). Treatment was permanently discontinued in 5 (15%) pts after a median of 3 doses due to grade ≥2 keratopathy. It was withheld in 9 (26%) due to grade ≥2 keratopathy. Four (12%) pts required dose reduction due to keratopathy (Table 1). Of 14 pts in whom BLMF was held or permanently discontinued due to keratopathy, 10 (77%) had disease progression within a median of 1 month (range 0.4-2.5) following BLMF interruption. Among 11 pts (32%) responded to BLMF, 45% developed grade 2 or higher keratopathy after the first dose compared to 13% of pts who were refractory to BLMF (n=23). The median duration of response was 4 (95%CI: 0.7-NR) months. Among responders, 2 pts (18%) had to permanently discontinue BLMF due to keratopathy, with subsequent progression from partial remission at 1.4 and 4.6 months following its discontinuation. Additionally, 73% of responders required dose withholding due to keratopathy and of those, 50% had evidence of disease progression within a median of 1.2 month (range: 0.7-1.7) following BLMF interruption. DISCUSSION & CONCLUSIONS : Despite efficacy in pts with advanced MM, in routine practice, ocular toxicity of BLMF is a major challenge and a deterrent to its continuous use. While frequent ocular toxicity-related interruptions were observed at expected rates, permanent treatment discontinuation due to keratopathy was observed in a higher proportion of pts than that encountered in the DREAMM-2 trial. Larger prospective studies are warranted to accurately discern the effects of ocular toxicities on outcomes of pts treated with BLMF. Figure 1 Figure 1. Disclosures Patel: Emmecell: Consultancy; GlaxoSmithKline: Consultancy; Senju Pharmaceuticals: Consultancy; Santen, Inc.: Consultancy. Kumar: Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Roche-Genentech: Consultancy, Research Funding; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; Antengene: Consultancy, Honoraria; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Ailawadhi: AbbVie: Consultancy; Xencor: Research Funding; Medimmune: Research Funding; Genentech: Consultancy; Ascentage: Research Funding; Takeda: Consultancy; Sanofi: Consultancy; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Cellectar: Research Funding; BMS: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; GSK: Consultancy, Research Funding; Karyopharm: Consultancy; Beigene: Consultancy. Bergsagel: Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; Genetech: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Kapoor: Karyopharm: Consultancy; AbbVie: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding.

Abstract INTRODUCTION POEMS syndrome is a paraneoplastic disorder that results in multi-organ disease, and Vascular endothelial growth factor (VEGF) is associated with disease activity. There is evidence that the cytokine Interleukin- 6 (IL-6) stimulates VEGF production in several cancer cell lines. It has also been suggested that patients with POEMS syndrome experience symptoms improvement when IL-6 levels are low. Our study aims to understand the role of IL-6 and whether it is a contributor to disease activity in POEMS syndrome. METHODS We conducted a retrospective study of patients (pts) seen at Mayo Clinic within 1 year of diagnosis for POEMS syndrome with an IL-6 level within 3 months of diagnosis between 01/01/2011-05/31/2021 were included. Our database comprised 358 pts,7 were excluded because did not have official diagnosis of POEMS syndrome. Two hundred and ninety-nine pts did not have IL-6 testing within 90 days of diagnosis and were excluded. Clinical data was abstracted from our electronic medical record. Descriptive statistics were used for IL-6 levels. The majority of IL-6 laboratory testing for pts utilized the same reference range. A minority of pts had alternate reference ranges; thus, all IL-6 values and reference ranges were fold-corrected to have a unified reference to determine elevation in IL-6. High IL-6 was considered any value above the normal range, also analyzed pts in quartiles and deciles. Hematologic, VEGF and PET response criteria were utilized to group pts into responders or non-responders. Time to progression (TTP) and overall survival (OS) was calculated using the Kaplan Meier method. Differences between curves were by log rank. Statistical analysis was conducted via JMP software package (SAS, Cary, NC, USA). Univariate analysis was done to determine the prognostic value of IL-6. Statistical significance is considered with p-values &lt;0.05. RESULTS 52 patients from among 352 met criteria and were included for analysis. Twenty-one patients (40%) had elevated IL-6 levels (IQR 5.67, 22.6) at the time of diagnosis. The fold elevation about normal was typically not very high, with a median of 3.1-fold elevation (IQR 1.3, 4.4; and range 1.0, 8.3). The percentage of males in the high IL-6 versus the normal Il-6 group were 90% versus 65%, p=0.03. Compared to normal IL-6, those with elevated IL-6 values had more instances of hepatomegaly (43% vs 16%, p=0.03), ascites (28% vs 6%, p=0 .04), abnormal DLCO (26% vs 3%, p=0.03), mixed bone lesions (57% vs 29%, p= 0.04), and lower serum albumin (range 2.3-3.5 vs 2.5-4.5 g/dL, p=0.0008).There was a trend for lower VEGF values among the high IL-6 group, but this did not meet statistical significance (median 250 vs 438 pg/mL). There was no significant difference in the time to progression between pts with high versus normal IL-6 levels (HR 1.36; p=0.456). Overall survival was significantly longer in pts with normal IL-6 compared to pts with high IL-6 levels (Median OS 67.4 vs 47.8 months, p=0.02). Forty-two pts were evaluated for VEGF response after treatment; there was a significant improvement in time to progression (85.5 vs 14.9 months, p=0.03) in pts who were VEGF responders. Among pts evaluable for a hematologic response (n=15), time to progression in pts with baseline high IL6 was longer in hematologic responders compared to non-responders (p=0.02). Conclusion Although OS was longer for pts with normal IL-6, elevated IL-6 at the time of diagnosis does not prevent pts from achieving disease remission. The study demonstrates that response to treatment rather than interleukin-6 levels in pts with POEMS syndrome is more prognostic. This study is an important initial step into understanding the utility of IL-6 in POEMS syndrome. Figure 1 Figure 1. Disclosures Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Aurora Biopharma: Other: Stock option; Ionis Pharmaceuticals: Other: Advisory Board; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria. Kumar: Oncopeptides: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Merck: Research Funding; Antengene: Consultancy, Honoraria; Tenebio: Research Funding; Novartis: Research Funding; Roche-Genentech: Consultancy, Research Funding; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Dingli: GSK: Consultancy; Novartis: Research Funding; Janssen: Consultancy; Sanofi: Consultancy; Apellis: Consultancy; Alexion: Consultancy. Lin: Janssen: Consultancy, Research Funding; Merck: Research Funding; Novartis: Consultancy; Legend: Consultancy; Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Vineti: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding; Sorrento: Consultancy; Gamida Cell: Consultancy; Takeda: Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Dispenzieri: Pfizer: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Alnylam: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding.