Mead‐Harvey, Carolyn

Abstract Context Enhanced Recovery After Surgery (ERAS) is a multimodal protocol aimed to improve quality of postoperative recovery, minimize complications, and optimize overall self-regulation. Preoperative gabapentin decreases postoperative pain but can be associated with prolonged postoperative somnolence and respiratory depression risk. Although it is known that gabapentin affects the postoperative course, it is unclear if the timing of preoperative administration affects this finding. Objectives This study aims to assess the optimal preoperative timing for gabapentin administration in patients undergoing gynecologic surgery to minimize postoperative somnolence risk. Methods A retrospective cohort study evaluated patients who underwent major gynecologic surgery and received preoperative gabapentin. Patients were grouped based on timing from gabapentin administration to surgical incision (<4 h group vs. ≥4 h group). Preoperative, intraoperative, and postoperative data were abstracted and compared. Univariate associations between the timing of gabapentin administration and the patient and surgical characteristics and outcomes were tested utilizing two-sample equal-variance t-tests, linear model ANOVA, or Fisher’s exact tests. Associations between the timing of gabapentin administration and the time until the Richmond Agitation Sedation Scale (RASS) score of 0 were modeled utilizing linear regression, adjusted for age, initial postoperative anesthesia care unit (PACU), RASS score, and postoperative narcotics. Results Each group contained 127 patients. Demographics were similar except for age (<4 h group mean=44.2 years; ≥4 h group mean=40.5 years; p=0.021), chronic pain (<4 h group=17.6%; ≥4 h group=43.3%; p<0.001), and surgical indication (<4 h group=pelvic pain [29.1%]; ≥4 h group=pelvic pain [51.2%]; p=0.007). The <4 h group had a similar postoperative narcotic administration (<4 h group mean morphine milligram equivalents [MME]=3.667; ≥4 h group mean MME=4.833; p=0.185). The minutes from surgical closure until the patient received a RASS score of 0 and initial PACU pain score (Visual Analogue Scale [VAS]) were similar. The initial PACU oxygen administration volume, hours from surgical closure until the patient transitioned to room air, and initial PACU respiratory rate were similar. The PACU duration, admission secondary to somnolence, and initial PACU Glasgow Coma Scale (GCS) score showed no difference. Postoperative nausea/vomiting was decreased in the ≥4 h group (<4 h group=24.4%; ≥4 h group=13.4%; p-value=0.036), and urinary retention (<4 h group=14.2%; ≥4 h group=5.5%; p-value=0.033) was decreased in the ≥4 h group. Conclusions The timing of gabapentin administration less than or more than 4 h preoperatively in patients ≥18 years does not significantly affect postoperative somnolence or respiratory depression. Further, it does not have a significant effect on GCS scores or VAS scores.

Abstract Background Although uncommon, nontuberculous mycobacterial infections (NTMI) of the upper extremity cause significant morbidity based on their natural history, delay in diagnosis, prolonged duration of antimicrobial therapy often combined with surgical debridement, and functional loss. Herein we describe our experience with such infections. Methods Records for adult patients from two academic, tertiary facilities with culture-proven NTMI involving the upper extremity were retrospectively reviewed. Demographic information, co-morbidities, laboratory and microbiological evaluation, management, and outcomes were extracted. Patients were analyzed based on pathogen identified and immune suppression. Results 77 patients were identified. The mean age was 59 years and 65% of patients were male. 48% reported a preceding injury, with the hand being most frequently involved (58%). 41% were considered immune compromised; 19% of them were organ transplant recipients. Mean symptom duration prior to presentation was 203 days. Mean time to culture identification was 33 days, and 25 different species of NTM were identified (subcategorized as rapid or slow growers). 77% had solitary lesions, with cutaneous/subcutaneous location as the most common site. All patients underwent surgical debridement with four undergoing amputation to control infection. 69% received combination antimicrobial therapy for a mean duration of 184 days. Immunosuppressed patients were treated with antimicrobial therapy for a longer duration (mean 243 vs 155 days). One-third of patients experienced complications and/or recurrence regardless of organism type. Conclusion NTMI of the upper extremity is often misdiagnosed leading to significant delays in appropriate management. Knowledge of its protean manifestations and early consideration in the differential diagnosis of chronic, painful swelling of the hand or wrist, nodular or inflammatory lesions, or septic arthritis is crucial. A low threshold for surgical or biopsy with specimens sent for histopathology as well as microbiologic analysis is warranted. A combined approach with surgical debridement and prolonged combination antimicrobial therapy is necessary for optimal outcomes; however, adverse reactions from such therapy are commonly encountered. Disclosures All Authors: No reported disclosures

Abstract TGFβ plays a pivotal role in the pathobiology of myelofibrosis (MF) by not only promoting bone marrow fibrosis (BMF) but also by enhancing the dormancy of normal but not MF hematopoietic stem cells (HSCs). TGFβ has also previously been reported to inhibit normal megakaryocyte (MK) production (Bruno et al Blood 1998). TGFβ1 promotes the synthesis of collagen by normal human mesenchymal stromal cells (MSCs). Treatment of MSCs with AVID200, a potent TGFβ1/3 protein trap, significantly decreased MSC proliferation, phosphorylation of SMAD2, and collagen expression. Robust expression of pSMAD2 was observed in the absence of exogenous TGFβ in normal donor or MF-MKs, Addition of AVID200 to MKs decreased pSMAD2 without affecting total SMAD2/3 and led to increased numbers of MKs. Treatment of MF MNCs with AVID200 also led to increased numbers of progenitor cells with wild type JAK2 and a reduction of mutated colonies. A phase 1b trial of AVID200 (NCT03895112) was performed and completed in INT-2/high risk MF patients resistant/intolerant to ruxolitinib (rux); baseline platelet count of ≥ 25 x 10 9/L, and grade 2/3 BMF. Subjects received AVID200 intravenously on Day 1 of a 21 day cycle. Response was assessed by IWG/ELN criteria after 6 cycles of AVID200. Subjects attaining at least a CI or SD with a decrease in BMF by ≥1 grade, continued AVID200. We previously presented the results of the dose escalation study (Mascarenhas ASH 2020) demonstrating that AVID200 was well tolerated without dose limiting toxicities at 3 tested dose levels (Lots A and B) in dose cohorts of 180 mg (A), 550 mg (A)/70 mg (B), and 180 mg (B). Here we report updated safety and efficacy results of the phase 1b dose expansion stage at the two highest doses tested (70 mg (B) and 180 mg (B). Twenty-two subjects were enrolled (1 withdrew before receiving treatment) and 9 were treated with AVID200 in the dose escalation phase and 12 in the dose expansion phase [Table1]. Median time after rux discontinuation was 7.4 months (0.5-59.9). The most common mutations observed at baseline in this cohort included JAK2V617F (71%), TET2 (29%) ASXL1 (24%) and CALR (19%). (Fig 1) No DLTs were observed and Grade 3/4 AEs were observed in 16 (76.2%) subjects. Grade 3/4 non-hematologic AEs were observed in 8 (38.1%) subjects and included one subject in each case (epistaxis, mucositis, extraocular muscle paresis, fatigue, rash, duodenal hemorrhage, gastric hemorrhage, urinary tract infection, and syncope). Grade 3/4 hematologic AEs were anemia (6; 28.3%) and thrombocytopenia (2; 14.3%) [Table 2]. No fatal events were observed. The median number of cycles received was 5 (range 2 - 13) and 7 (33%) patients received more than 6 cycles. For dose levels 2-3 at cycle 7, a CI was attained in one subject at dose level 2 [anemia, spleen and TSS], 5 subjects had SD, 3 subjects had PD and two subjects with 10% and 15% blasts at screening developed MPN-BP while on study based on central review. Reasons for discontinuation by local PI included PD (n=8), lack of response (n=5), study completed (n=2), other (n=2), patient decision (n=1). Median % change in palpable spleen length was +10% (range -70% to +150%) and TSS change was -50% (-100% to +185.7%) The median platelet count at baseline was 114 x 10 9/L (range: 28-695) and 215 x 10 9/L (range: 66-263) after cycle 6 in 7 evaluable subjects (Fig 2A). Notably, 17 subjects had an increase in platelets from baseline during treatment and two subjects normalized their platelet counts. Maximum changes in platelets from baseline across all cycles was +63.8% [range -15.7%, +505.5%] (Fig 2B). Paired bone marrow biopsy pathology samples for 12 subjects were available for central review and showed no significant changes in BMF score or MK histo topography at end of treatment compared to baseline. All patients had elevated plasma levels of TGF β1, but not TGFβ2/β3 levels as detected by ELISA, which were dramatically reduced 21 days after the last dose of AVID200. AVID200 a TGFβ1/3 protein trap is well tolerated and clinical responses at cycle 7 of therapy in this advanced MF patient population were limited as judged by IWG/MRT response criteria. However, AVID200 therapy resulted in significant reduction in serum TGFβ levels and improvements in platelet counts indicating that TGF β1 plays a pivotal role in MF leading to thrombocytopenia which can be reversed with AVID200 therapy. We conclude that AVID200 may best be employed in combination therapy approaches in thrombocytopenic MF patients. Figure 1 Figure 1. Disclosures Mascarenhas: Constellation: Consultancy, Membership on an entity's Board of Directors or advisory committees; Promedior: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Geron: Consultancy, Research Funding; Forbius: Research Funding; Genentech/Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; PharmaEssentia: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Galecto: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Prelude: Consultancy; Kartos: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; CTI Biopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Geron: Consultancy; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Merus: Research Funding. Palmer: PharmaEssentia: Research Funding; Sierra Oncology: Consultancy, Research Funding; Incyte: Research Funding; CTI BioPharma: Consultancy, Research Funding; Protagonist: Consultancy, Research Funding. Kuykendall: Celgene/BMS: Honoraria; Pharmaessentia: Honoraria; Novartis: Honoraria, Speakers Bureau; Protagonist: Consultancy, Research Funding; Incyte: Consultancy; Abbvie: Honoraria; Blueprint: Honoraria. Mesa: Genentech: Research Funding; Promedior: Research Funding; Samus: Research Funding; Gilead: Research Funding; CTI: Research Funding; Abbvie: Research Funding; Sierra Oncology: Consultancy, Research Funding; Celgene: Research Funding; Novartis: Consultancy; Pharma: Consultancy; CTI: Research Funding; Constellation Pharmaceuticals: Consultancy, Research Funding; AOP: Consultancy; La Jolla Pharma: Consultancy; Incyte Corporation: Consultancy, Research Funding. Rampal: Stemline: Consultancy, Research Funding; Memorial Sloan Kettering: Current Employment; BMS/Celgene: Consultancy; Abbvie: Consultancy; CTI: Consultancy; Novartis: Consultancy; Disc Medicine: Consultancy; Blueprint: Consultancy; Pharmaessentia: Consultancy; Incyte: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy; Constellation: Research Funding; Kartos: Consultancy; Sierra Oncology: Consultancy. Gerds: PharmaEssentia Corporation: Consultancy; Sierra Oncology: Consultancy; CTI BioPharma: Research Funding; Constellation: Consultancy; Celgene/Bristol Myers Squibb: Consultancy; AbbVie: Consultancy; Novartis: Consultancy. Yacoub: Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; CTI Biopharma: Membership on an entity's Board of Directors or advisory committees; ACCELERON PHARMA: Membership on an entity's Board of Directors or advisory committees; Agios: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Dynavex: Current equity holder in publicly-traded company; Cara: Current equity holder in publicly-traded company; Ardelyx: Current equity holder in publicly-traded company; Seattle Genetics: Honoraria, Speakers Bureau; Incyte: Consultancy, Honoraria, Speakers Bureau; Hylapharm: Current equity holder in publicly-traded company. Talpaz: Imago: Consultancy; Constellation: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Other: Grant/research support ; Celgene: Consultancy. Komrokji: Acceleron: Consultancy; Taiho Oncology: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy; BMSCelgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; PharmaEssentia: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Geron: Consultancy; Jazz: Consultancy, Speakers Bureau. Kremyanskaya: Astellas: Research Funding; Astex: Research Funding; Chimerix: Research Funding; Bristol Myers Squibb: Research Funding; Constellation: Research Funding; Protagonist Therapeutics: Consultancy, Research Funding; Incyte: Research Funding. Salama: Mayo Clinic: Current Employment, Other: Mayo Clinic had the contractual work for the central pathology review for this study and I was one of the reviewing pathologists; Constellation Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Hoffman: Kartos Therapeutics, Inc.: Research Funding; Protagonist Therapeutics, Inc.: Consultancy; Novartis: Other: Data Safety Monitoring Board, Research Funding; AbbVie Inc.: Other: Data Safety Monitoring Board, Research Funding. OffLabel Disclosure: AVID200 is a TGFb trap and is in clinical testing for fibrotic diseases. It does not have an approved indication at this time.

Abstract MAJIC is a phase II trial of Ruxolitinib (RUX) vs Best Available Therapy (BAT) in polycythemia vera (PV) patients with resistance/intolerance to Hydroxycarbamide (HC). This analysis involved a primary comparison between the RUX and BAT arms for quality of life (QOL) durability over 60 months, with secondary comparisons of best QOL response within first year in complete hematologic responders (CR) vs a group of partial or no response (NR/PR). This is a unique analysis due to cross-over nature of prior studies. Patients were stratified by treatment arm with either RUX or BAT. QOL was assessed over 0-60 months using the Myeloproliferative Neoplasms Symptom Assessment Form (MPN-SAF). MPN-SAF Total Symptom Score (TSS) was computed as the average of all completed items multiplied by 10 (scale 0-100, higher score represents higher symptom burden). Estimates of change from baseline and between arm differences in change by timepoint were made using a linear mixed model with compound symmetry covariance structure, which included covariates for categorical time point, treatment arm, and the interaction between time point and treatment arm. The difference between arms in proportion of patients with best post-baseline TSS response of 50% or greater was testing using a Chi-square test. In this study, 147 of the 190 trial patients had started treatment and completed at least their baseline assessment and were included in the analysis with 39 patients completing through 60 months. Groups were comprised of 76 patients in the RUX group (31 females, 45 males, mean age 64.2 [SD 11.4]) and 71 patients in the BAT group (29 females, 42 males, mean age 64.6 [SD 11.5]). Symptom scores at baseline were similar between arms, with MPN-SAF weight loss the only statistically significant difference (BAT 0.7 [SD 1.71] vs RUX 1.7 [SD 2.83], p=0.02). Significant, durable improvements in TSS were noted in RUX patients with symptom improvements lasting a mean of 52 months. BAT patients experienced a worsening of their symptom burden with improvements back to baseline notable at 56 months (Figure 1). MPN-SAF TSS there were significant differences between treatment arms in change from baseline to months 2-32, 44, and 48 (all p&lt;0.05). In all months the trend was in the direction of larger improvement in the RUX arm, with the point estimate for difference ranging from 2.1 (at month 56) to 11.3 (at month 4) with most prominent changes occurring between months 2 and 32. Of the 80 patients with MPN-SAF TSS scores at baseline and at least one post-baseline timepoint, 13/41 (31.7%) BAT and 24/39 (61.5%) RUX patients had TSS reduction of 50% or greater in at least one time point, which was statistically significant (p=0.008). In regard to specific symptoms, there was a statistically significant between arm difference seen in over 5 timepoints for fatigue, early satiety, night-sweats, itching, bone pain, and weight loss. Comparing MPN-SAF TSS in Complete Response (CR n=51) vs Non-Responder/Partial Responders (NR/PR) (n=96) there was no difference at baseline in any of the measures at any time point. A possible limitation of this study is the potential for bias in which the patients who were lost to follow up could be different then the patients who remained in the study through all 60 months which would affect generalization, particularly at time points at the later years. The novel findings of this investigation demonstrate that Ruxolitinib ameliorates the PV symptom burden in patients resistant or intolerant to HC in a robust durable manner over at least four years, while patients receiving BAT have worsening of their symptom burden over this same time period. Figure 1 Figure 1. Disclosures Mead: Novartis: Consultancy, Honoraria, Speakers Bureau; Celgene/BMS: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria. Yap: Celgene: Honoraria; Faron Pharmaceuticals: Honoraria. Scherber: Incyte Corporation: Current Employment, Current holder of stock options in a privately-held company. Knapper: Novartis: Consultancy, Research Funding, Speakers Bureau; Astellas: Ended employment in the past 24 months, Speakers Bureau; Pfizer: Consultancy, Speakers Bureau; Jazz: Consultancy, Speakers Bureau. Drummond: Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; CTI: Membership on an entity's Board of Directors or advisory committees. McMullin: Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Other: clinical trial support, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AOP Orphan: Research Funding, Speakers Bureau. Mesa: CTI: Research Funding; AOP: Consultancy; Celgene: Research Funding; Novartis: Consultancy; Gilead: Research Funding; Pharma: Consultancy; Promedior: Research Funding; Constellation Pharmaceuticals: Consultancy, Research Funding; CTI: Research Funding; Samus: Research Funding; Sierra Oncology: Consultancy, Research Funding; Genentech: Research Funding; Incyte Corporation: Consultancy, Research Funding; Abbvie: Research Funding; La Jolla Pharma: Consultancy. Harrison: CTI BioPharma: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead Sciences: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Shire: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Geron: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Constellation Pharmaceuticals: Research Funding; Galacteo: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte Corporation: Speakers Bureau; Sierra Oncology: Honoraria; Promedior: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AOP Orphan Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Keros: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau.

Abstract Background: Self-reported financial hardship (FH) amongst cancer patients is increasingly becoming a challenge for patients, caregivers, and healthcare providers. FH not only leads to financial struggles, significant lifestyle changes, and emotional distress, but also contributes to treatment noncompliance, affecting clinical outcomes. As treatment costs rise, it is crucial to develop efficient methods to proactively identify and alleviate FH in hematology practice. One potential approach is utilizing automated processes to identify those at highest risk of FH. At Mayo Clinic, screening for FH involves using a single financial strain question 'How hard is it for you to pay for the very basics like food, housing, medical care, and heating?' which all cancer patients answer annually as part of the institution's Social Determinants of Health (SDOH) assessment. Answers are on a five-point scale including not hard at all, not very hard, somewhat hard, hard, and very hard. In this study, we assess the prevalence and predictors for FH (denoted by a response of "Very hard" "Hard" or "Somewhat hard") amongst the Mayo Clinic hematologic malignancy patient population. Our study objective was to determine if this automated process could identify those at risk for FH. Methods: Patients who received care for hematologic malignancies (lymphoma, leukemia, plasma disorders, myelodysplastic/myeloproliferative disorders, and other heme malignancies) at any of the Mayo Clinic cancer centers (Minnesota, Arizona, and Florida) and who had completed the SDOH screen at least once were included in this study. The electronic medical record (EMR) and Mayo Clinic Cancer Registry were utilized to extract demographic and disease variables. Patient's home zip code was used to determine rural/urban residence, distance from cancer center, and the Area Deprivation Index (ADI), a measure of socioeconomic disadvantage based on home zip code (ranging from 1-100, with 100 representing the most disadvantaged). Multivariable logistic regression modeling was used to examine predictor variables for FH in this patient population. Results: The final cohort included 10,024 patients from 2018 to 2020. Median age was 64.6 years (IQR 58.1,73.7), 58% were male, and 79% married. Race/ethnicity composition was 94% White (n=9,268), 2.5% Black (n=246), 0.4% American Indian/Alaskan Native (44), and 4% Hispanic (n=360). Fifty-six percent of patients had Medicare and 41% had commercial insurance. Fifty percent were retired, 40% were working/students, and 72% were urban residents. Mean ADI was 41.2. Fifty-six percent of patients had lymphomas, 23.5% had plasma cell disorders, 8.5% had leukemias, 6.8% had other hematological malignancies, and 5.5% had myelodysplastic/myeloproliferative neoplasms. FH was reported by 12.8% (n=1286) of the patients. Table 1 shows the results of the multivariable model. A significantly higher likelihood of endorsing FH was noted in Hispanic vs non-Hispanics, Black and American Indian/Alaskan Native groups vs whites, Disabled/Unemployed vs working, Medicaid, Medicare, and Self-Pay groups vs commercial insurance, higher ADI (5 th quintile vs 1 st), and myelodysplastic/myeloproliferative disorder and other hematologic malignancy vs lymphoma patients. Older age, being retired, and living farther from the cancer center were associated with significantly less likelihood of endorsing FH. Conclusion: Our study used automated data extraction from the EMR to efficiently identify predictors of FH in hematologic cancer patients. Employing a dichotomized and automated "flag" for FH, particularly if incorporated in the EMR, could ease the identification of SDOH issues, facilitate timely connection to appropriate resources, and help provide better patient-centered care. Figure 1 Figure 1. Disclosures Ailawadhi: Sanofi: Consultancy; Cellectar: Research Funding; Karyopharm: Consultancy; Ascentage: Research Funding; Genentech: Consultancy; Janssen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Beigene: Consultancy; GSK: Consultancy, Research Funding; AbbVie: Consultancy; Medimmune: Research Funding; Pharmacyclics: Consultancy, Research Funding; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Xencor: Research Funding. Fonseca: OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy; BMS: Consultancy; Mayo Clinic in Arizona: Current Employment; Aduro: Consultancy; AbbVie: Consultancy; GSK: Consultancy; Merck: Consultancy; Juno: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Novartis: Consultancy; Bayer: Consultancy; Celgene: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy. Griffin: Exact Sciences: Research Funding.

Abstract The myeloproliferative neoplasms (MPN) polycythaemia vera (PV) and essential thrombocythaemia (ET) are associated with significant symptom burden with impaired quality of life (QoL). In the MAJIC study patients refractory/intolerant to hydroxycarbamide (HC), were randomised to treatment with ruxolitinib (Rux) or best available therapy (BAT). In this unique and comprehensive analysis we explore quality of life (QoL) outcomes in the MAJIC study using the MPN10 Self-Assessment Form (SAF) in particular using this unique dataset to explore for the first time differences between JAK2 ET vs PV, and JAK2 ET vs CALR ET at baseline and 12 months of therapy with Rux or BAT. Methods 306 patients were randomised, (190, PV and 116 ET arm) and followed for 5 years, no cross over was permitted. MPN10 SAF was assessed at baseline, 2, 4, 8 and 12 months in both arms. Equal variance two sample t-tests were used to test differences between diagnosis groups (JAK2 ET vs PV & JAK2 ET vs CALR ET) in baseline scores and 12-month change from baseline scores, here negative change from baseline indicates improvement. QoL data was also collected using MD Anderson Symptoms Inventory (MDASI) and 5 level EQ5D at the same time points as the MPN10 SAF. Results JAK2 ET vs PV 110 participants with JAK2 mutation were included in the analysis for JAK2 ET vs PV. 81 (74%) had PV and 29 (26%) had JAK2 ET; 44 patients (54.3%) with PV and 18 (62.1%) patients with ET were randomised to Rux, and 37 (45.6%) patients with PV and 11 (37.9%) with ET to BAT. Baseline mean total symptom score (TSS) was similar (JAK2 ET: 18.9. SD 18.4 and PV: 23.7, SD 17.81 (p=0.27). Mean for pruritus was 1.9 (SD 2.47) in JAK2 ET and 3.5 (SD 2.99) in PV (p=0.03). Other baseline scores were comparable: mean scores for fatigue was 3.3 (SD 3.07) for JAK2 ET vs 4.7 (SD 2.68) for PV, for early satiety was 2.6 (SD 2.78) for JAK2 ET vs 2.1 (SD 2.40) for PV, for abdominal discomfort 3.0 (SD 3.34) for JAK2 ET vs 2.0 (SD 2.78) for PV, for inactivity was 2.4 (SD 2.86) for JAK2 ET vs 3.2 (SD 2.72) for PV; for concentration problems was 2.2 (SD 2.51) for JAK2 ET vs 2.9 (SD 3.11) for PV, for night sweats was 1.6 (SD 2.20) for JAK2 ET vs 2.1 (SD 2.66) for PV, for bone pain was 1.8 (SD 2.97) for JAK2 ET vs 2.0 (SD 2.86) for PV, for fever was 0.5 (SD 1.46) for JAK2 ET vs 0.5 (SD 1.70) for PV and for weight loss was 0.3 (SD 0.73) for JAK2 ET vs 1.0 (SD 2.15) for PV (p&gt;0.05 for all). Concerning change at 12 months, mean (SD) change in TSS was -0.5 (SD 10.86) for JAK2 ET (n=21) vs -0.8 (SD 13.32) for PV (n=66) (P=0.93). Mean for individual symptom scores at 12 months were also not significantly different between the two phenotypic disease group (P&gt;0.05 for all parameters). (Fig 1, top panels). CALR vs JAK2 ET Exploring symptoms for CALR vs JAK2 ET: 45 patients were included; 9/16 (56.3%) CALR and 18/29 (62.1%) JAK2 patients were assigned to Rux (p=0.70). 10/16 (62.5%) CALR ET and 19/29 (65.5%) JAK2 ET. Baseline mean total symptom score (TSS) was similar (JAK2 ET: 18.9, SD 18.4 and CALR ET: 18.8, SD 16.99 (p=0. 0.98). All other baseline scores were comparable in this intolerant/ refractory population (p&gt;0.05 for all. Concerning change at 12 months, mean (SD) change in TSS was -0.5 (SD 10.86) for JAK2 ET vs -2.8 (SD 9.96) for CALR ET (P=0.56). Mean for individual symptom scores at 12 months were also not significantly different between the two phenotypic disease group (P&gt;0.05 for all parameters); the mean change in night sweats approached but did not reach significance similar (JAK2 ET: 0.2 (SD 1.89) and CALR ET: 1.8 (SD 3.07) (p=0. 0.06). (Fig 1, lower panels). Preliminary analysis of the MDASI and EQ5D-5L data at baseline and at 12 months shows similar results and will be presented. Conclusion This first analysis of its kind compared the symptom burden and other parameters of QoL of patients with PV and ET, resistant/intolerant to HC shows that with the exception of pruritus at baseline there were no substantial differences between JAK2 ET or PV. Equally changes in symptom burden across 12 months did not substantially differ between JAK2 ET or PV. A second analysis of JAK2 vs CALR ET also failed to show substantial differences. This data should be expanded in a larger cohort but supports a symptom continuum of JAK2 ET and PV and also JAK2 vs CALR ET. Figure 1 Figure 1. Disclosures Mead: Abbvie: Consultancy, Honoraria; Celgene/BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Speakers Bureau. Yap: Faron Pharmaceuticals: Honoraria; Celgene: Honoraria. Knapper: Novartis: Consultancy, Research Funding, Speakers Bureau; Astellas: Consultancy, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Speakers Bureau; Pfizer: Consultancy, Speakers Bureau. Drummond: CTI: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Mesa: Pharma: Consultancy; CTI: Research Funding; Abbvie: Research Funding; Genentech: Research Funding; Gilead: Research Funding; AOP: Consultancy; Incyte Corporation: Consultancy, Research Funding; Sierra Oncology: Consultancy, Research Funding; Novartis: Consultancy; La Jolla Pharma: Consultancy; Samus: Research Funding; Promedior: Research Funding; Constellation Pharmaceuticals: Consultancy, Research Funding; Celgene: Research Funding; CTI: Research Funding. Scherber: Incyte Corporation: Current Employment, Current holder of stock options in a privately-held company. McMullin: Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AOP Orphan: Research Funding, Speakers Bureau; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Other: clinical trial support, Research Funding. Harrison: CTI BioPharma: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Promedior: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead Sciences: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Keros: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AOP Orphan Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Shire: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sierra Oncology: Honoraria; Constellation Pharmaceuticals: Research Funding; Incyte Corporation: Speakers Bureau; Geron: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Galacteo: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

6594 Background: Patient reported financial hardship (FH) in cancer care is a growing challenge for patients, their caregivers and healthcare providers. As treatment costs escalate, it is imperative to develop effective strategies to proactively recognize and mitigate FH within oncology practice. Using automated processes to screen and refer patients to appropriate resources is a potential option. At Mayo Clinic, screening for FH involves using a single financial strain question ‘ How hard is it for you to pay for the very basics like food, housing, medical care, and heating?’ completed by all cancer patients annually as part of the Social Determinants of Health (SDOH) assessment. In this study, we describe the prevalence and predictors for FH (denoted by the answer ‘hard and very hard’) in our patient population. Methods: Patients receiving cancer care at the three Mayo Clinic sites (Minnesota, Arizona, and Florida) who completed the FH screen at least once were included in this study. Demographics (age, gender, race/ ethnicity, insurance, employment status, marital status, and zip code) and disease state data for included patients was extracted from the EMR and Mayo Clinic Cancer Registry. Disease state was categorized by type of cancer (hematological or solid malignancy) and cancer stage. Zip code was used to derive median income, rural/urban residence and distance from the cancer center. Multivariable logistic regression models were utilized to examine factors associated with FH. Results: The final study cohort included 31,969 patients with median age 66 years (IQR 57,73), 51% females, and 76% married. Race/ethnicity composition was 93% White, 3% Black, and 4% Hispanic. 52% of patients had Medicare and 43% had commercial insurance. Other notable factors included 48% retired, 41% working/ students, 76% married, and 72% urban residents. Median time from cancer diagnosis was 1.1 year (IQR 0.1, 3.8) and median income was $64,406 (IQR 53,067, 82,038). 31% of patients had hematological malignancies, 20% of the cancers for which staging information was available were metastatic. FH was reported by 4% (n = 1194) of the patients. A significantly higher likelihood of endorsing FH (p < 0.001 for all) was noted in Hispanic (OR 1.64), Black (OR 1.84), American Indian/Alaskan native (OR 2.02), below median income (OR 1.48), rural (OR 1.17), self-pay (OR 2.77), Medicaid (OR 2.29), Medicare (OR 1.43), unemployed/disabled (OR 2.39), single (OR 2.07), or divorced (OR 2.43) patients. Older age, being retired, and living farther from the cancer center were associated with significantly less likelihood of endorsing FH. Conclusions: Our study successfully leveraged the EMR to identify key sociodemographic groups more likely to report FH. An electronic trigger to flag such patients at high-risk of FH and proactively address FH is currently being developed.

Introduction: Serum folate (SF), vitamin B12 (B12), and iron deficiency (def) are common causes of nutritional anemias (NA). These deficiencies are usually multifactorial, with nutritional and non-nutritional causes playing a role. SF, B12, and iron levels are usually ordered in the setting of anemia, and malnutrition with or without neurologic symptoms. Clinical evidence suggests that these def have a strong dietary component and socioeconomic status (SES). The relationship of NA and area-based SES in the US has not been studied. We aimed to determine the relationship of SES with the prevalence of NA. Methods: We performed a cross-sectional analysis of adult patients with SF, B12 and iron levels at Mayo Clinic Arizona and Florida between 2010 and 2018. Race was classified using the NIH criteria. Normal laboratory values were determined according to our lab reference and the US NHANES III. SF levels (mcg/Lt) were defined as deficient &lt;4, normal ≥4.0, and excess ≥20. B12 levels (ng/L) as deficient &lt;150, borderline 150-400, normal &gt;400-900, and excess ≥900. Iron def was determined by ferritin levels (mcg/L) as low &lt;24, normal 24-336, elevated &gt;336 for men, low &lt;11, normal 11-307, elevated &gt;307 for women. Area-Level SES indicators: Median Household income (MHI), unemployment rate (UR), median gross rent month (MGRM), % uninsured, median house value (MHV), % high school; were geocoded by zip code using the 2014 American Community Survey. Demographics and clinical variables were compared between groups by chi-square test for frequency data or Kruskal Wallis rank-sum test for continuous variables. Results: 202,046 samples from 128,084 patients were analyzed. In the sample-level analysis, there were statistically significant associations between SES and SF def; all SES indicators except UR for B12 def; and no differences for iron def, except % uninsured (Table 1). There was no statistically significant interaction between race and SES for SF def and iron def. Race was a statistically significant modifier between B12 def and MHI (p&lt;0.001), % uninsured (p=0.002), and MHV (p=0.007). Asian and Other race had an increase in odds of B12 def with increasing MHI (Asian OR=1.11 , Other OR=1.18); white race had a decrease in odds of B12 def with increasing MHI (OR=0.95 for a $10,000 increase in MHI). Conclusions: We show significant relationships between SES and NA in the US. Differences were observed between SF def and all the SES indicators without race interactions. There were significant interactions between B12 def, race and SES for pts of White, Asian and Other race. There were no differences between SES and race for iron def. These relationships confirm that NA are related to area-level SES and other social determinants of health. Research regarding the causes of these disparities on a population level are needed. Disclosures No relevant conflicts of interest to declare.

Background: The COVID pandemic has resulted in significant changes many aspects of daily living. To understand the impact that COVID-19 has had on the myeloproliferative neoplasm (MPN) patient population, we conducted an internet based survey. Methods: Survey: This survey was hosted Mayo Clinic's secured REDCap system for online surveys with a link to the survey alongside a brief description posted via the www.mpnqol.com website, as well as other MPN organizational partners. Survey responses were completely anonymous. Questions included MPN-Total symptom score (TSS), NCCN distress thermometer (NCCN-DT), questions regarding impact on medical care, and questions from CDC COVID-19 community survey question bank regarding the impact of social distancing as well as changes in health behaviors. Distress thermometer, MPN-TSS, and/or questions related to the impact of COVID-19 on MPN treatment were analyzed by: MPN diagnosis, among those with ET, PV, or MF diagnoses; medication status; stay at home order; community spread; and country. Associations were tested using Kruskal-Wallis or Wilcoxon rank sum tests (for continuous variables) or Fisher Exact tests (for categorical variables). Analysis: Results: Patient Demographics (Table 1): 1560 people responded to the survey, 1217 were eligible for analysis. Median age was 62 (range: 21-93), and 298 (24.6%) respondents were male. There were respondents from USA, Australia, Canada, Netherlands, Ireland, and UK. 233 patients (19.2%) have myelofibrosis, 419 (34.6%) polycythemia vera and 543 (44.8%) essential thrombocythemia. At the time of the COVID outbreak, 1026 (84.3%) were on MPN directed medical therapy, including ruxolitinib (15.3%), interferon (14.7%), hydroxyurea (42.7%) and ASA (47.7%). 165 (13.6%) respondents received COVID testing, of which 5 had positive tests. Impact on MPN Care (Table 2): We sought to understand how patient's clinical care changed. Over half respondents who spoke to their MPN doctor had a telemedicine visit after COVID19 (57.1%). 422 (36.5%) patients spaced out visits, of which 99 (22.7%) felt there were consequences. A change in therapy due to COVID-1 occurred in only 5.4% of patients. MPN Symptom Burden and QoL: Data captured on the NCCN-DT had a median of 4 (0-10). MPN-SAF-TSS composite score was collected in 1150 respondents, median score was 26 (0-90). These scores are higher than those previously reported. Pandemic Impact on Lifestyle (Table 1): 595 (49.5%) of patients report living in a community where there is significant COVID-19 spread. 946 (78.9%) reported that COVID-19 has impacted their day to day life. 198 (17.2%) of patients agreed, or strongly agreed that COVID had a significant impact on their finances. 799 (67.8%) had a stay at home order. Of those who quarantined (112), the median duration was 30d (1-120). The majority of people increased hand-washing, and cleaning habits. 954 (81.7%) respondents reported wearing masks in public. 908 (76.8%) reported increased stress from social distancing. The majority of respondents report using healthy coping habits, such as reaching out to friends/loved ones, breathing and relaxation, as well as healthy diet. Less than 15% of patients report unhealthy coping strategies, such as use of opioids, benzodiazepines, alcohol, or cannabis Factors associated with response: There were no differences in responses based on type of MPN. If a patient was on medication, they were more likely to have spoken to their provider (p&lt;0.001). If there was a stay at home order in place, there was a higher MPN-TSS score (p&lt;0.001). If the respondent lived in an area of high community spread, they had a higher NCCN-DT score (p&lt;0.001). Patients in the USA had a higher NCCN-DT score (p=0.001), were more likely to stretch out the duration of time between visits (p&lt;0.001) and less likely to have a telemedicine visit (p&lt;0.001). Although fewer respondents in the USA thought COVID-19 was a serious disease (p=0.02), a higher percentage of respondents wore masks (p &lt;0.001). Conclusions: In our survey of MPN patients, there were many changes noted in clinical care. The use of telemedicine was common, and at least a third had significant changes in their care. Most experienced increased stress, however, employed healthy coping strategies. Only a minority of patients had a COVID test, and only 5 were positive, further data will be needed to understand the impact of COVID infection. Figure 1 Disclosures Harrison: Roche: Honoraria; Incyte Corporation: Speakers Bureau; Celgene: Honoraria, Research Funding, Speakers Bureau; Sierra Oncology: Honoraria; Promedior: Honoraria; Shire: Honoraria, Speakers Bureau; AOP Orphan Pharmaceuticals: Honoraria; Novartis: Honoraria, Research Funding, Speakers Bureau; Janssen: Speakers Bureau; Gilead Sciences: Honoraria, Speakers Bureau; CTI Biopharma Corp: Honoraria, Speakers Bureau. Pemmaraju:Affymetrix: Other: Grant Support, Research Funding; Plexxikon: Research Funding; Incyte Corporation: Honoraria; Roche Diagnostics: Honoraria; Blueprint Medicines: Honoraria; Novartis: Honoraria, Research Funding; SagerStrong Foundation: Other: Grant Support; DAVA Oncology: Honoraria; Samus Therapeutics: Research Funding; Cellectis: Research Funding; Daiichi Sankyo: Research Funding; Celgene: Honoraria; AbbVie: Honoraria, Research Funding; Stemline Therapeutics: Honoraria, Research Funding; MustangBio: Honoraria; LFB Biotechnologies: Honoraria; Pacylex Pharmaceuticals: Consultancy. Vannucchi:Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees; Blueprint: Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Gupta:Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol MyersSquibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Research Funding; Pfizer: Consultancy. Mascarenhas:Celgene, Prelude, Galecto, Promedior, Geron, Constellation, and Incyte: Consultancy; Incyte, Kartos, Roche, Promedior, Merck, Merus, Arog, CTI Biopharma, Janssen, and PharmaEssentia: Other: Research funding (institution). Shimoda:Kyowa Hakko Kirin Co., Ltd.: Research Funding; Pfizer Inc.: Research Funding; Otsuka Pharmaceutical: Research Funding; Celgene: Honoraria; Perseus Proteomics: Research Funding; PharmaEssentia Japan: Research Funding; AbbVie Inc.: Research Funding; Astellas Pharma: Research Funding; Merck & Co.: Research Funding; CHUGAI PHARMACEUTICAL CO., LTD.: Research Funding; Bristol-Myers Squibb: Honoraria; Takeda Pharmaceutical Company: Honoraria; Novartis: Honoraria, Research Funding; Shire plc: Honoraria; Asahi Kasei Medical: Research Funding; Japanese Society of Hematology: Research Funding; The Shinnihon Foundation of Advanced Medical Treatment Research: Research Funding. Kiladjian:AOP Orphan: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Komatsu:AbbVie: Other: member of safety assessment committee in M13-834 clinical trial.; Otsuka Pharmaceutical Co., Ltd., Shire Japan KK, Novartis Pharma KK, PharmaEssentia Japan KK, Fuso Pharmaceutical Industries, Ltd., Fujifilm Wako Pure Chemical Corporation, Chugai Pharmaceutical Co., Ltd., Kyowa Hakko Kirin Co., Ltd., Takeda Pharmaceutica: Research Funding; Meiji Seika Pharma Co., Ltd.: Patents & Royalties: PCT/JP2020/008434, Research Funding; PPMX: Consultancy, Research Funding; Otsuka Pharmaceutical Co., Ltd., PharmaEssentia Japan KK, AbbVie GK, Celgene KK, Novartis Pharma KK, Shire Japan KK, Japan Tobacco Inc: Consultancy; Takeda Pharmaceutical Co., Ltd, Novartis Pharma KK, Shire Japan KK: Speakers Bureau. Abello:Takeda: Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Research Funding; Dr. Reddy's: Consultancy, Research Funding; Abbvie: Consultancy, Research Funding. Gomez-Almaguer:Amgen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Scherber:Incyte Corporation: Current Employment, Current equity holder in publicly-traded company. Mauro:Novartis: Consultancy, Honoraria, Other: Travel, Accommodation, Expenses, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Other: Travel, Accommodation, Expenses, Research Funding; Sun Pharma/SPARC: Research Funding; Takeda: Consultancy, Honoraria, Other: Travel, Accommodation, Expenses, Research Funding; Pfizer: Consultancy, Honoraria, Other: Travel, Accommodation, Expenses, Research Funding. Mesa:Samus Therapeutics: Research Funding; Novartis: Consultancy; Genentech: Research Funding; Incyte: Research Funding; Bristol Myers Squibb: Research Funding; AbbVie: Research Funding; CTI BioPharma: Research Funding; Sierra Oncology: Consultancy; LaJolla Pharmaceutical Company: Consultancy; Promedior: Research Funding.

Background Patients with myeloproliferative neoplasms (MPNs) are faced with severe disease-related fatigue among a range of other constitutional and spleen-related symptoms. The MPN-Symptom Assessment Form (SAF) is recommended for use to characterize symptom burden (Scherber R, et al. Blood 2011). Within the SAF, a profile of 18 symptom items are evaluated ranging in severity from 0 (absent) to 10 (worst imaginable). The SAF is often summarized to the MPN-SAF Total Symptom Score (TSS) for analysis purposes - a single computed sum of the 10 most clinically meaningful symptom scores, including fatigue (Emanuel R, et al. J Clin Oncol 2012). Though the SAF includes a fatigue item, initial deployments of the MPN-SAF TSS incorporated a 0-10 scaled fatigue item taken from the Brief Fatigue Inventory (BFI; Mendoza T, et al. Cancer 1999). A subsequent version of the MPN-SAF TSS for use within myelofibrosis clinical trials (called the MFSAF v4; Gwaltney C, et al. Leuk Res 2017) employed a harmonized fatigue item. This analysis employing data from two studies was carried out to assess the use of the SAF fatigue item within the MPN-SAF TSS for consistency with the MFSAF v4. Methods Both BFI and SAF fatigue items were included in an initial online survey evaluating disease burden among patients with MPNs. Participants were assigned to survey variants as a function of their age. Survey variants included those to receive 1 instance of either the BFI or SAF fatigue item, instances of both BFI and SAF, or 2 instances of the same fatigue item. Surveying was aimed to assess the worst symptom experience in the patient's last 24 hours. Additionally, an independent survey assessing the impact of COVID-19 among MPN patients was deployed using the SAF fatigue item for the MPN-SAF TSS. This modified version was then used to test internal validity. Pearson correlation (r) and t-tests were used to assess association, Bland-Altman methods were used to evaluate systematic agreement between BFI and SAF fatigue scores, and Cronbach's alpha was used to measure internal consistency. Results There were 229 participants assigned both BFI and SAF fatigue items within the same survey. Among them, 51% (n=117) received the BFI item first and 49% (n=112) the SAF item first. No difference was seen between first and second fatigue scores (mean difference [first-second] = 0.00; 95%CI -0.18, 0.17). BFI and SAF fatigue scores were highly correlated (r=0.88, p&lt;0.001) and showed 88.7% agreement in categorizing severe versus non-severe fatigue (score ≥ 7 versus &lt; 7). Overall concordance in severity category was 73.4% (category [score range]: absent [0]; mild [1-3]; moderate [4-6]; severe [7-10]). Constructing the MPN-SAF TSS using the BFI and SAF fatigue components separately, the original and modified MPN-SAF TSS were nearly identical (r=0.997, p&lt;0.001), and had equivalent internal consistency (both Cronbach's alpha=0.88). The Bland-Altman plot further indicates the 2 fatigue measures have high agreement with no evidence of directional bias and negligible overall bias (Figure 1: regression slope = -0.04, p=0.25; mean difference=0.22; 95%CI 0.05, 0.39). Within the COVID-19 survey (n=1217), the modified version of the MPN-SAF TSS was consistent with known correlates among disease characteristics (Emanuel R, et al. J Clin Oncol 2012). For example, more severe MPN-SAF TSS scores were highly correlated with low quality-of-life (n=1156, r = -0.50, p&lt;0.001), and associated with those reporting spleen enlargement (n=301) versus not (n=617) (p&lt;0.001; mean difference=7.7; 95%CI 5.4, 10.1). Conclusion The BFI and SAF fatigue items are highly consistent in raw score, severity category, and in contribution to the MPN-SAF TSS. There was no order effect seen in which fatigue item was asked first. The independent COVID-19 survey using the modified MPN-SAF TSS was validated and shows high internal consistent. In the ongoing development to capture the symptom experience, this analysis shows disease-related fatigue is equivalently measured using the SAF fatigue survey item in harmonization with the MFSAF v4. Disclosures Mesa: Bristol Myers Squibb: Research Funding; CTI BioPharma: Research Funding; Promedior: Research Funding; AbbVie: Research Funding; Samus Therapeutics: Research Funding; Genentech: Research Funding; Incyte: Research Funding; LaJolla Pharmaceutical Company: Consultancy; Novartis: Consultancy; Sierra Oncology: Consultancy.