McGraw Hill Research Articles

Genetic Consequences of the Transatlantic Slave Trade in the Americas

According to historical records of transatlantic slavery, traders forcibly deported an estimated 12.5 million people from ports along the Atlantic coastline of Africa between the 16th and 19th centuries, with global impacts reaching to the present day, more than a century and a half after slavery’s abolition. Such records have fueled a broad understanding of the forced migration from Africa to the Americas yet remain underexplored in concert with genetic data. Here, we analyzed genotype array data from 50,281 research participants, which—combined with historical shipping documents—illustrate that the current genetic landscape of the Americas is largely concordant with expectations derived from documentation of slave voyages. For instance, genetic connections between people in slave trading regions of Africa and disembarkation regions of the Americas generally mirror the proportion of individuals forcibly moved between those regions. While some discordances can be explained by additional records of deportations within the Americas, other discordances yield insights into variable survival rates and timing of arrival of enslaved people from specific regions of Africa. Furthermore, the greater contribution of African women to the gene pool compared to African men varies across the Americas, consistent with literature documenting regional differences in slavery practices. This investigation of the transatlantic slave trade, which is broad in scope in terms of both datasets and analyses, establishes genetic links between individuals in the Americas and populations across Atlantic Africa, yielding a more comprehensive understanding of the African roots of peoples of the Americas.

Measurement of Convective Heat Transfer Coefficients With Supercritical CO2 Using the Wilson-Plot Technique

Abstract This paper describes the measurement of convective heat transfer coefficients and friction factors for sCO2 flowing in a smooth tube and compares the results with published correlations for validation. The paper also describes the Heat Exchange and Experimental Testing (HEET) rig recently designed and built at the U.S. Department of Energy’s (DoE’s) National Energy Technology Laboratory (NETL) in Morgantown, WV. The Wilson-plot technique used for measuring the heat transfer coefficients is described along with the data reduction process. The Wilson-plot technique was chosen as the basis for the design of NETL’s HEET rig. Advantages of the Wilson-plot technique include the (1) ability to measure high convective heat transfer coefficients accurately, (2) ability to measure average heat transfer coefficient for complicated heat exchange geometries like those produced using additive manufacturing, (3) ability to measure heat transfer coefficients on both sides of a heat exchanger independently, and (4) simplicity of experimental setup. Capabilities of the HEET rig include pressure to 24 MPa (3500 psig), temperature to 538 °C (1000 °F), mass flow rate to 1.5 kg/s (3 lb/s), and Re to 500,000. The rig is designed to operate with pure CO2 or a mixture of CO2 and up to 10% N2 by volume to study the impact of gas mixtures typical of direct-fired sCO2 power cycles on the convective heat transfer and pressure drop. Preliminary tests in the HEET rig were performed with smooth stainless-steel tube and pure CO2, and the results were compared with published correlations for Nusselt number (Nu) and friction factor. Over a Reynolds number (Re) range from 58,000 to 228,000, measured Nu was compared to predictions using the Dittus and Boelter equation (Kreith and Bohn, 1993, “Principles of Heat Transfer, West Publishing Company”) within 5% and measured friction factors were compared to predictions using the McAdams correlation (“McAdams, 1954, “Heat Transmission,” 3rd ed., McGraw Hill, New York)” for smooth tube to be within 5%.

Dream Machines: The Motorcar as Sign of Conquest and Destruction in F. Scott Fitzgerald's <em>The Great Gatsby</em>

Dream Machines: The Motorcar as Sign of Conquest and Destruction in F. Scott Fitzgerald's <em>The Great Gatsby</em>

Re-thinking pedagogical content knowledge for physical education teachers – implications for physical education teacher education

ABSTRACT Background: In this conceptual paper, we contribute to the discussion of pedagogical content knowledge (PCK) in physical education and physical education teacher education (PETE). There are two main limitations in the work inspired by Shulman’s [1987. “Knowledge and Teaching: Foundations of the New Reform.” Harvard Educational Review 57 (1): 1–21] concepts content knowledge (CK) and PCK. First, CK is exclusively interpreted as knowledge in and about movement, and excludes knowledge through movement. Second, contemporary understandings of CK and PCK have been mainly from a behaviour analytic perspective. By only adopting a behavioural perspective of CK, i.e. a perspective which aims to change students’ behaviours without necessarily changing knowledge or understanding, pre-service teachers are unlikely to reflect on context and culture or how these affect the students with whom they will work. Purpose: The purpose of this paper is to add a new perspective to the contemporary discussion of PCK in physical education and PETE through elaborating on how PCK could be conceptualised ‘phronetically’. We believe that contextual and situational foci of a phronetic approach constitute an important dimension of teacher knowledge, and that this dimension is not captured or made visible by behaviour analytic discourse of PCK in movement cultures. Method: For the conceptual task of expanding our understanding of PCK, we have been inspired by Thomas [2007. Education and Theory: Strangers in Paradigms. Berkshire: McGraw Hill], Shoemaker, Tankard, and Lasorsa [2004. How to Build Social Science Theories. Thousand Oaks, CA: Sage], and Whetten [1989. “What Constitutes a Theoretical Contribution?” Journal Academy of Management Review 14 (4): 490–495] and their ideas of building theory through borrowing, reflective thinking, and metaphors. Results: We outline four major assumptions made about PCK in the behaviour analytic research on physical education and PETE: 1. Physical education teachers must know how to perform activities with the correct technique, know the tactics and have knowledge about rules and etiquette; 2. Physical education teachers must know how to detect errors and design task progressions. 3. Physical education teachers must know how to select and modify appropriate tasks as well as give feedback. 4. Physical education teachers’ level of CK and PCK can be quantitatively measured. Conclusions: From a phronetic perspective, we suggest that PCK could also involve: contextual characteristics for ‘new’ and integrative movement cultures; interpretation of students’ actions; identification and action on diversity during physical education teaching; development of a sensitivity for morally ‘right’ actions; and management of uncertainty involved in physical education teaching.

Update on the NAEPPCC Asthma Guidelines: The wait is over, or is it?

As an asthma specialist, one is often asked to discuss the asthma guidelines. The challenge that one faces is deciding which guidelines to use as a resource for discussion or presentation. It often comes down to the following questions: What is the question? What message does one want to deliver? Where is the most current information? and Who is the target audience? This commentary will help answer those questions as it guides the reader through the new update of the National Heart, Lung and Blood Institute/National Asthma Education and Prevention Program guidelines and then outlines differences between 2 widely used strategies for the management of asthma: the Global Initiative for Asthma and the National Heart, Lung and Blood Institute/National Asthma Education and Prevention Program Guidelines for the Diagnosis and Management of Asthma.

P009 FINANCIAL VOLATILITY OF INFLAMMATORY BOWEL DISEASES VS OTHER CHRONIC GASTROINTESTINAL DISEASES - USING THE BETA COEFFICIENT TO CATEGORIZE GI DISORDERS

Abstract Symptomatic chronic diseases differ in their propensity for serious costly morbidity. Reliable and predictable deterioration presentations can be associated with very narrow margins between symptoms and the onset of serious complications. The inflammatory bowel diseases (IBD): Crohn’s Disease and Ulcerative Colitis are examples of this. As a result, they have high cost per capita with significant variation in that cost. Reliable metrics for assessing the relative volatility of chronic diseases are lacking. In finance, the volatility of a stock is measured using the beta coefficient, a measure of the relative volatility of an individual stock in relation to the that of an index.[i] By definition, the specific index has a beta of 1.0, and individual stocks are ranked according to how much they deviate from the market based on their beta coefficient. A stock that demonstrates more volatility than the market over time has a beta above 1.0. We postulated that chronic gastrointestinal diseases can be profiled using a similar measurement of volatility based on cost. Using a data set of 40,523 members obtained from Health Care Service Corporation, which included professional, facility and pharmacy claims for calendar year 2017, we calculated an index and beta rating for the major gastrointestinal disorders: gastroesophageal reflux disease (GERD), Peptic Ulcer disease (PUD), Gastritis, Celiac disease, Pancreatitis, Irritable Bowel Syndrome (IBS), Crohn’s disease (CD), Ulcerative colitis (UC), Colon Polyps and Diverticulitis: Method: The Total Disease Specific Cost (TDSC) was calculated from claims data for each condition using ICD - Codes.A GI Disease index (GIDI) was created by calculating the TDSC of all of the above conditions. The GIDI TDSC was then segregated into deciles.The cost/decile was then analyzed for each condition and compared against the GI IndexA beta rating (Beta) was calculated using Standard Deviations of the relative cost/decile (SDCD) as follows: Beta = SDCD (Illness)/SDCD (Index) Results: Using this methodology, the GI Index and individual beta ratings are numerically and graphically shown in the figures. Whereas CD and UC have strongly positive Beta scores, the remainder of the GI illnesses do not as compared to the GI Index. Figure 1. Major Gastrointestinal illnesses’ Beta Rating with respect to GI Index Table 1. Summary of major Gastrointestinal illnesses’ cost by decile and Beta Rating Conclusions: Gastrointestinal disorders can be categorized, based on their volatility, into a beta rating[ii]. Disorders associated with high cost and high variability in cost have a high-beta rating compared to the GI disorder index. This is a critical finding as high-beta conditions are those toward which management payments should be focused as they benefit most from patient engagement, care coordination and care managment programs to improve outcomes and control costs. [i] Sharpe W. Portfolio theory and capital markets. New York: McGraw Hill, 1970.[ii] Kosinski L, Brill J; Clinical Gastroenterology and Hepatology Vol. 14, No. 12, P1751-1752

Evaluasi Komposisi Tubuh dan Pemanfaatan Nutrien di Ambing Kambing Peranakan Etawah Laktasi yang Diberi Pakan Fermentasi Limbah Tempe

The aim of this study was to evaluate the body composition, nutrient uptake in mammary gland and milk amino acid profile of lactating Etawah Crossbred goats fed by tempeh by product. Twelve lactating goats were randomly allocated into three groups which fed by ration containing concentrate (R1), concentrate plus 25% fresh tempeh waste (R2) and concentrate plus 25% fermented tempeh waste (R3). Tempeh waste was fermented by Aspergillus niger. Kinggrass was given 50% of the total ration for all groups. Urea space technique was used to measure body composition before and after the experiment was done. Milk production was calculated two times a day during two months after giving birth (postpartum). Total milk protein and amino acid, whey and casein were analyzed by kjeldahl method and amino acid analyzer respectively. Nutrient uptake in mammary gland was calculated base on Fick principles. Result showed that there was significantly different on body weight, but on body water, protein and fat had no significant difference between the treatments. The best milk production was found in fermented tempeh waste group. Concentration of glutamic acid was dominant than other essential amino acids in whole milk and casein but there were no significant difference between groups for those essential amino acid profile. It was concluded that fermented tempeh waste could substitute 50% of total concentrate and had the highest body weight, milk yield and nutrient uptake in the mammary gland of lactating Etawah Crossbred goats.
 
 Key words: body composition, fermentation, lactating PE goat, nutrient uptake, tempeh waste
 
 DAFTAR PUSTAKA
 Astuti DA & Sastradipradja D. 1998. Measurement of body composition using slaughter technique and urea-space in local sheep. Indonesian Journal of Veterinary Science. 3: 1-9
 Astuti DA & Sastradipradja D. 1999. Evaluation of body composition using urea dilution and slaughter technique of growing Priangan sheep. Media Veteriner. 6 (3) : 5-9.
 Astuti DA, Sastradipradja D & Sutardi T. 2000. Nutrient balance and glucose metabolism of female growing, late pregnant and lactating ettawah crossbred goats. Asian-Australasian Journal of Animal Sciences. 13:8: 1068-1077
 Astuti DA& Wina E. 2002. Protein balance and excreation of purine derivatives in urine of lactating etawah crossbred goats fed with tempe waste. Jurnal Peternakan dan Veteriner. 7(3) : 162-166
 Astuti DA, Baba AS & Wibawan IWT. 2011. Rumen fermentation, blood metabolites, and performance of sheep fed tropical browse plants. Media Peternakan. 34 (3) : 201-206
 Astuti DA & Sudarman A. 2015. Status fisiologi, profil darah dan komposisi tubuh domba yang diberi minyak lemuru tersaponifikasi dan disalut dengan herbal. Buletin Peternakan. 39 (2): 116-122.
 Badan Pengendalian Pengendalian Dampak Lingkungan Daerah. 2000. Laporan Pertanggungjawaban Pembangunan Instalasi Pengolahan Limbah Cair Industri Tahu Tempe PRIMKOPTI Ngoto Yogyakarta. Yogyakarta (ID) : Setwilda Propinsi DIY
 Bishop JM, Hill, DJ, & Hosking CS. 1990. Goat milk does not suppress the immune system. Journal of Pediatrics. 116: 862-867
 Bruhn JC, FST & Davis CA. 1999. Dairy goat milk composition. https://drinc.ucdavis.edu/goat-dairy-foods/dairy-goat-milk-composition
 Cant JP, DePeters EJ & Baldwin RL, 1993. Mammary amino acid utilization in dairy cows fed fat and its relationship to milk protein depression. Journal of Dairy Science. 76 (3) :762-774
 Chaiyabutr N, Komolvanich S, Preuksagorn S & Chanpongsang S. 2000. Comparative studies on the utilization of glucose in the mammary gland of crossbred holstein cattle feeding on different types of roughage during different stages of lactation. Asian-Australasian Journal of Animal Sciences. 13 (3): 334 – 347.
 Laconi EB, & Jayanegara A. 2015. Improving nutritional quality of cocoa pod (theobroma cacao) through chemical and biological treatments for ruminant feeding: in vitro and in vivo evaluation. Asian-Australasian Journal of Animal Sciences Open Access. http://dx.doi.org/10.5713/ajas.13.0798.
 Lehninger AL. 1982. Principles of Biochemistry. New York (US): Woth Publisher, Inc.
 NRC. 1990. Nutrient Requirement of Goat. Washington (US): National Academy of Science.
 Panaretto BA & Till A.R.. 1963. Body compositition in vivo. II. The composition of mature goats and its relationship to the antypyrene, tritiated water and acetyl-4-aminoantipyrene spaces. Australian Journal of Agricultural Research. 14 (6): 926 – 943
 Park YW. 1991. Goat milk as a substitute for those who are lactose intolerant. Journal of Dairy Science 74:3326-3333
 Riis PM. 1983. Dynamic Biochemistry of Animal Production. New York (US): Elsevier Science 
 Rovanis. 1995. Letters in applied microbiology. Journal of Milk and Food Technology. 20 (3): 164-167
 Steel RGD & Torrie JH, 1993. Principles and Procedures of Statistics. New York (US): McGraw Hill Book Co. Inc.
 Shapiro BA, Harrison RA & Walton JR, 1982. Clinical Application of Blood Gas. 3rd ed. London (UK): Book Medical Publishers, Inc.
 Waghorn GC & Baldwin, RL. 1984. Model of metabolic flux within mammary gland of the lactating cows. Journal of Dairy Science. 67: 531-544

О ПРИМЕНИМОСТИ ФАЗОВОГО МЕТОДА СКАНИРОВАНИЯ В АНТЕННЫХ РЕШЕТКАХ ИМПУЛЬСНЫХ ЛЧМ-РАДАРОВ

О ПРИМЕНИМОСТИ ФАЗОВОГО МЕТОДА СКАНИРОВАНИЯ В АНТЕННЫХ РЕШЕТКАХ ИМПУЛЬСНЫХ ЛЧМ-РАДАРОВ

RORA Is a Potential Prognostic Biomarker and Therapeutic Target for Patients with Acute Myeloid Leukemia

One strategy to improve precision medicine in acute myeloid leukemia (AML) is to further our understanding of the biological factors that influence pharmacologic efficacy. In a recent study, whole transcriptome analysis was conducted using pre- and post-treatment samples from a cohort of relapsed or refractory (R/R) AML patients treated with mitoxantrone, etoposide, cytarabine (MEC), and ixazomib. Logistic regression and linear discriminant analysis identified RORa as a predictor of response to treatment (Advani, et al., Clin Cancer Res, 2019, 4231-4237). RORa is not frequently mutated in AML but is described to be a tumor suppressor in patients with solid tumors. In the latter patient population, increased RORa expression is also associated with improved survival. We first retrospectively evaluated the prognostic significance of RORa in a larger cohort of R/R patients with AML (BeatAML). Second, we characterized RORa expression in cellular models of AML and sought to determine whether a commercially available RORa/RORγ agonist, SR1078, has anti-proliferative capacity in leukemia cell lines. For the correlative survival analyses, RORa mRNA expression and clinical information was downloaded from the BeatAML cohort (Tyner, et al., Nature, 2018, 526-531). Patients were categorized into high expressers (≥ median) and low expressers (<median). In total, 121 R/R patients were investigated. The mean age of diagnosis was 62 ± 12 years, and 58% of patients were male. The most common specific diagnosis at inclusion was AML with myelodysplasia related changes (28%), followed by AML with mutated NPM1 (18%) and therapy-related myeloid dysplasia (13%). Over half of the analyzed samples were peripheral blood (58%), and the remaining samples were either bone marrow aspirate (58%) or from leukapharesis (2%). As a whole, the median mRNA expression levels of RORa in patients with R/R AML (n=121) compared to healthy subjects (n=21) were 6.6 log2 CPM vs. 2.5 log2 CPM (P<.0001). After grouping patients into low RORa expresser and high RORa expresser groups, patients with above median expression of RORa were found to have significantly longer overall survival (19 mo. vs. 13 mo.; P=.0052; Figure 1). During normal hematopoiesis, we observed that RORa expression decreased with the stages of cellular maturation and is highly expressed in cells of AML patients with complex karyotype (BloodSpot, 2018). We noted that RORa mRNA expression varied across patients suggesting differences between AML subtypes. Expression levels were also confirmed to be different across AML subtypes in vitro by using cell line models (K-562, KG-1, OCI-AML3, U-937, THP-1, MOLM-13). In particular, analysis of TCGA data showed higher mean RORa expression in DNMT3A mutant (MT) (626 RPKM) compared to mean RORa expression in CEBPAMT (243 RPKM; P=0.026), NPM1MT (167 RPKM; P=0.012), and FLT3MT (236 RPKM; P=.003) AML patients. We then analyzed the sensitivity of cell lines to the commercially available synthetic RORa /RORγ ligand, SR1078. Myeloid lineage cells U-937 and KG-1 were used as a cellular model. Cells in logarithmic phase were treated with increased concentrations of the RORa /RORγ agonist SR1078 (from 3 nM to 30 mM) for 24 hours. Cell viability was measured by MTT tetrazolium reduction assay. Maximal growth inhibition was reached at 30 µM for U-937 (80%) and KG-1 (75%), respectively. We then combined SR1078 with MEC to evaluate whether in vitro SR1078 increased MEC growth inhibition. The addition of SR1078 to MEC significantly decreased cell viability in KG-1 cells compared to MEC alone (82% vs. 19%, P=.029). Our study suggests that increased RORa expression may be associated with improved survival in patients with R/R AML and that RORa may be a potential therapeutic target in AML. Figure 1 Disclosures Gerds: Celgene Corporation: Consultancy, Research Funding; Imago Biosciences: Research Funding; Pfizer: Consultancy; CTI Biopharma: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Roche: Research Funding; Sierra Oncology: Research Funding. Mukherjee:Bristol-Myers Squibb: Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; Novartis: 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; Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; McGraw Hill Hematology Oncology Board Review: Other: Editor; Projects in Knowledge: Honoraria. Nazha:Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee; Tolero, Karyopharma: Honoraria; Abbvie: Consultancy; Daiichi Sankyo: Consultancy. Maciejewski:Alexion: Consultancy; Novartis: Consultancy. Sekeres:Millenium: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees. Advani:Abbvie: Research Funding; Pfizer: Honoraria, Research Funding; Glycomimetics: Consultancy, Research Funding; Amgen: Research Funding; Macrogenics: Research Funding; Kite Pharmaceuticals: Consultancy.

The Impact of Comorbidities and Organ Dysfunction Commonly Used for Clinical Trial Eligibility Criteria on Outcome in Acute Myeloid Leukemia (AML) Patients Receiving Induction Chemotherapy

Hobbs: SimulStat Inc.: Consultancy; Amgen: Research Funding. Mukherjee:Bristol-Myers Squibb: Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Projects in Knowledge: Honoraria; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; McGraw Hill Hematology Oncology Board Review: Other: Editor. Advani:Abbvie: Research Funding; Macrogenics: Research Funding; Pfizer: Honoraria, Research Funding; Amgen: Research Funding; Kite Pharmaceuticals: Consultancy; Glycomimetics: Consultancy, Research Funding. Gerds:Sierra Oncology: Research Funding; Imago Biosciences: Research Funding; Roche: Research Funding; CTI Biopharma: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Pfizer: Consultancy; Celgene Corporation: Consultancy, Research Funding. Nazha:Tolero, Karyopharma: Honoraria; MEI: Other: Data monitoring Committee; Novartis: Speakers Bureau; Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; Daiichi Sankyo: Consultancy; Abbvie: Consultancy. Sekeres:Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium: Membership on an entity's Board of Directors or advisory committees.

A Single Arm, Phase II Study of Eltrombopag to Enhance Platelet Count Recovery in Older Patients with Acute Myeloid Leukemia (AML) Undergoing Remission Induction Therapy

A Single Arm, Phase II Study of Eltrombopag to Enhance Platelet Count Recovery in Older Patients with Acute Myeloid Leukemia (AML) Undergoing Remission Induction Therapy

Predicting Response to Hypomethylating Agents in Patients with Myelodysplastic Syndromes (MDS) Using Artificial Intelligence (AI)

Introduction While the hypomethylating agents (HMAs) azacitidine (AZA) and decitabine (DAC) improve cytopenias and prolong survival in MDS patients (pts), response is not guaranteed. Timely identification of non-responders could prevent prolonged exposure to ineffective therapy, thereby reducing toxicities and costs. Currently no widely accepted clinical or genomic models exist to predict response or resistance to HMAs. We developed a clinical model to predict response or resistance to HMA after 90 days of initiating therapy based on changes in blood counts using time series analysis technology similar to the kind used in Apple's Siri or Google Assistant. In the setting of voice recognition, the sequence and context of words determines the meaning of a sentence; similarly, we hypothesized that the pattern of changes in MDS pts' blood counts would predict response or resistance early during treatment. Methods We screened a cohort of 107 pts with MDS (per 2016 WHO criteria) who received HMAs at our institution between February 2005 and July 2013 and had regular CBCs drawn during treatment. Mutations from a panel of 60 genes commonly mutated in myeloid malignancy were included. Responses were assessed after 6 months of therapy per International Working Group (IWG) 2006 criteria. Pts were divided randomly into training (80%) and validation (20%) cohorts. To address the potential for bias due to a small sample size, an oversampling algorithm was used to cluster similar pts based on their CBC data, Revised International Prognostic Scoring System (IPSS-R) score, and % bone marrow blasts at the time of diagnosis. CBC data from the first 90 days of treatment were fed into deep neural network (recurrent neural network) and decision tree algorithms, which were trained to predict whether pts would achieve a response (defined as complete remission (CR), partial remission (PR), or hematologic Improvement (HI)). Area under the curve (AUC) was used to assess model performance. Important features that impact the algorithm's predictions were extracted and plotted. Results 20747 unique data points were used, including CBC, clinical and genomic data. Among 107 pts, 61 (57.0%) received AZA only, 19 (17.8%) DAC only, 4 (3.7%) received both DAC and AZA, and 23 (21.5%) received HMA with an additional agent. Median age was 69 years (range: 37-100 years), and 27 (26.4%) were female. Forty pts (37.4%) were very low/low risk, 32 (29.9%) intermediate, 19 (17.8%) high, and 16 (14.9%) very high risk per IPSS-R. Responses included 23 (22.5%) CR, 2 (1.9%) marrow CR, 4 (3.9%) PR, and 20 (19.6%) HI. The most commonly mutated genes were ASXL1 (17.6%), TET2 (16.7%), SRSF2 (15.7%), SF3B1 (11.8%), RUNX1 (10.8%), STAG2(10.8%), and DNMT3A (10.8%). The median number of mutations per sample was 1 (range, 0-11), and 40 pts (39.2%) had > 3 mutations per sample. When trained using absolute values and changes in CBC values, the model's AUC was 0.95 in the training cohort and 0.83 in the validation cohort. When the cohort was oversampled to 1000 pts, the validation cohort AUC increased to 0.89. Feature extraction algorithms identified increases in MCV and RDW during weeks 2-8 of treatment, increased proportion of lymphocytes, decreased proportion of monocytes, and increased platelet counts during weeks 6-8 as factors favoring response to HMA. The model provides personalized, patient-specific predictions that correlate with blood counts (Figure 1). Conclusions We describe a machine learning model that monitors changes in blood counts during therapy with HMA to predict response or resistance to HMA in MDS pts. Such a model can be used to develop novel trial designs wherein pts predicted to not respond after 90 days of HMA treatment could be assigned to an investigational agent. Conversely, it would help inform the decision to continue HMA therapy in pts predicted to respond. Increasing sample size with oversampling dramatically increased model accuracy; a larger cohort of pts treated at different institutions is currently under development. Disclosures Sekeres: Millenium: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Mukherjee:Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Projects in Knowledge: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria; McGraw Hill Hematology Oncology Board Review: Other: Editor; Bristol-Myers Squibb: Speakers Bureau. Advani:Glycomimetics: Consultancy, Research Funding; Kite Pharmaceuticals: Consultancy; Amgen: Research Funding; Pfizer: Honoraria, Research Funding; Macrogenics: Research Funding; Abbvie: Research Funding. Maciejewski:Alexion: Consultancy; Novartis: Consultancy. Nazha:Novartis: Speakers Bureau; Tolero, Karyopharma: Honoraria; Abbvie: Consultancy; Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; Daiichi Sankyo: Consultancy; MEI: Other: Data monitoring Committee.

A Personalized Prediction Model to Risk Stratify Patients with Acute Myeloid Leukemia (AML) Using Artificial Intelligence

A Personalized Prediction Model to Risk Stratify Patients with Acute Myeloid Leukemia (AML) Using Artificial Intelligence

Geno-Clinical Model for the Diagnosis of Bone Marrow Myeloid Neoplasms

Geno-Clinical Model for the Diagnosis of Bone Marrow Myeloid Neoplasms

Interactions between Donor Activating Killer Immunoglobulin-like Receptors (KIRs) and Somatic Mutations and Their Association with Outcomes after Allogeneic Hematopoietic Cell Transplant for Acute Myeloid Leukemia (AML)

Background: Graft-versus-leukemia (GVL) responses after allogeneic hematopoietic cell transplantation (alloHCT) for AML are mediated by alloreactive donor-derived immune effector cells including T lymphocytes and natural killer (NK) cells. The function of NK cells is regulated by inhibitory and activating signals mediated through cell-surface receptors, including KIRs. Various models of NK cell alloreactivity have been associated with post-transplant outcomes, including leukemia relapse. However, these results have varied widely between different investigators employing similar models of NK cell alloreactivity. Assessment of somatic mutations in AML on post-transplant outcomes has not been investigated in the context of KIR profiles. Methods: In this single-institution retrospective cohort study, we investigated KIR haplotypes (haplotype AA vs. Bx [associated with multiple activating KIRs]; Cooley S., et al. Blood. 113:726-732. 2009) in the context of somatic mutations. We included 34 adult patients with AML who underwent alloHCT from a matched related donor from 2006 to 2013. A targeted multi-amplicon deep NGS panel of 79 commonly mutated genes in myeloid neoplasia was performed. Post-HCT outcomes were assessed based on mutational status and KIR haplotype with Kaplan-Meier method and log-rank test. Results: Median age at transplant was 54 (range 31-73). Cytogenetic risk groups were 9% favorable, 56% intermediate, and 35% poor based on 2017 ELN classification. HCT-CI scores included 26% low, 32% intermediate, and 41% high. Disease risk group defined by ASTCT included 71% low, 26% intermediate, and 3% high. Disease status at HCT included 74% CR1 and 26% CR2. Frequencies of somatic mutations prior to HCT were: 21% DNMT3A, 18% IDH2, 9% each for STAG2 and NRAS, 6% each for ASXL1, JAK2, PHF6, RUNX1, TET2, and 3% each for CBL, FLT3, NPM1, and U2AF1. Overall, 53% of patients had at least 1 mutation: 24%, 18%, 9%, and 3% of patients had 1, 2, 3, and 4 mutations, respectively. 41% were carriers of KIR haplotype AA, and 59% were haplotype Bx. Relapse (p=0.40), relapse-free (p=0.33), and overall survival (p=0.30) between haplotypes AA and Bx were not statistically different. However, when considering somatic mutations in the context of KIR haplotypes, those with any somatic mutation (n= 18) present had inferior relapse-free (p=0.002) and overall survival (p=0.002; figures A-B) as compared to those with none. Further assessment of outcomes was then considered for those who had the following poor prognostic mutations (n=12): ASXL1, DNMT3A, FLT3, NRAS, RUNX1, and TET2. KIR haplotype AA with one or more of these mutations was associated with inferior relapse-free (p=0.05) and overall survival (p=0.008). At median follow-up of 83 (range 66-137) months, 38% were alive. Non-relapse mortality rates were 21% (9-36) at 1 year and 29% (15-39) at 3 years. The most common causes of death for all patients were relapse (48%) followed by infection (33%). Conclusion: In presence of somatic mutations, carrying KIR haplotypes Bx was associated with better survival in AML post-alloHCT. The presence of multiple activating KIRs may also help mitigate the worse prognosis associated with some of the more deleterious somatic mutations in AML. These observations may have implications for improving patient risk stratification prior to transplant and optimizing donor selection. Future investigation with larger cohorts interrogating KIR haplotypes in the context of pre-transplant AML somatic mutations on post-transplant outcomes may further elucidate which patients may benefit most from transplant. Disclosures Nazha: Tolero, Karyopharma: Honoraria; Abbvie: Consultancy; Daiichi Sankyo: Consultancy; Novartis: Speakers Bureau; Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; MEI: Other: Data monitoring Committee. Mukherjee:Bristol-Myers Squibb: Speakers Bureau; Projects in Knowledge: Honoraria; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; McGraw Hill Hematology Oncology Board Review: Other: Editor; Pfizer: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees. Advani:Pfizer: Honoraria, Research Funding; Macrogenics: Research Funding; Glycomimetics: Consultancy, Research Funding; Kite Pharmaceuticals: Consultancy; Amgen: Research Funding; Abbvie: Research Funding. Gerds:Sierra Oncology: Research Funding; Incyte: Consultancy, Research Funding; CTI Biopharma: Consultancy, Research Funding; Imago Biosciences: Research Funding; Celgene Corporation: Consultancy, Research Funding; Roche: Research Funding; Pfizer: Consultancy. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Millenium: Membership on an entity's Board of Directors or advisory committees. Majhail:Mallinckrodt: Honoraria; Atara Bio: Consultancy; Anthem, Inc.: Consultancy; Nkarta: Consultancy; Incyte: Consultancy. Maciejewski:Novartis: Consultancy; Alexion: Consultancy.

Determinants of “Fitness” for Intensive Therapy Among Acute Myeloid Leukemia (AML) Patients

Background: Determining which AML patients (pts) are "fit" for intensive chemotherapy (IC), both in real-world scenarios and for clinical trial eligibility (particularly as it relates to regulatory indications) is challenging. While age, AML etiology, and cardiac function have been invoked, these oversimplify the clinical assessment and recommendation for IC vs. Non-IC (NIC). We compared baseline characteristics among pts receiving IC and NIC, and evaluated associations between treatment receipt and pt characteristics. Adjusting for treatment received, pts' attributes were further evaluated for association with overall survival (OS). Methods: Adult (≥18 years) AML pts who received IC or NIC at Cleveland Clinic from 2003 to 2019 were included. The following characteristics were analyzed: age, sex, self-reported race, insurance, etiology of AML, comorbidities, hepatic and renal function tests, left ventricular ejection fraction (LVEF), and corrected QT interval (QTc). AML risk was categorized according to the 2017 European LeukemiaNet (ELN) risk stratification. Fisher's exact and Welch's t-tests were performed to compare baseline characteristics. Multivariate logistic and Cox regression were used to identify significant prognostic factors for treatment receipt and OS, respectively. The Kaplan-Meier method estimated distributions of OS, which were compared among pt cohorts using the log-rank test. Classification and regression tree (CART) analysis was performed to characterize the best decision process used to select pts for NIC. Results: Of 1,082 AML pts analyzed, 901 (83.2%) were treated with intensive, cytarabine-based chemotherapy, and 181 (16.8%) with non-intensive (low-dose cytarabine or hypomethylating agents) regimens. As expected, baseline characteristics were significantly imbalanced between groups; pts receiving NIC were more likely to be older (P<.001), have ≥3 comorbidities (P<.001), another cancer (P=.02), myocardial (P<.001), renal (P=.006) conditions, abnormal renal (creatinine (P=.004), creatinine clearance by Cockcroft-Gault (CrCl, P<.001)), and abnormal liver function (bilirubin (P=.03)). And less likely to have de novo disease (P<.001), favorable ELN risk (P=.008), private insurance (P<.001), and mild cardiac abnormalities (QTc 450-480ms (P<.001), LVEF 50-40% (P<.001)) (Tables 1 & 2). Multivariable stepwise regression revealed independent associations with the following attributes (favoring NIC over IC): older age (OR=1.10, P<.001), etiology (prior MDS vs. de novo; OR=2.52 , P<.001), liver (bilirubin >1.5xULN vs. normal; OR=3.04, P=.02), renal (CrCl <60 ml/min vs. normal; OR=3.17, P=.002), myocardial conditions (atrial fibrillation/congestive heart failure/coronary artery disease vs. normal); OR=2.33, P=.001) and mild cardiac abnormalities (QTc 450-480ms vs. normal: OR=0.46, P=.01; LVEF 50-40% vs. normal: OR=0.33, P=.03). Clinically, in making decisions about NIC, the first step is age, followed by comorbidity. CART analyses revealed that when a comorbidity is identified, age is then re-assessed; depending upon the type of comorbidity present, various age thresholds determined NIC vs. IC. With a median follow-up of 12.9 (IQR: 4.67-43.13) months, the median OS for the cohort was 15.1 months (range 0.03-189.25). OS was significantly different between groups (median OS, NIC vs. IC: 4.9 vs. 20.4 months, P<.001). In multivariate analyses controlling for treatment (IC vs. NIC), age, sex, insurance, number of comorbidities, AML etiology, and ELN risk, having another cancer (HR= 0.97, P=.78), renal (HR=1.10, P=.56) or myocardial comorbidity (HR= 0.94, P=.60) were not significantly associated with OS. Liver (bilirubin >1.5xULN: HR=1.70, P=.01), renal (creatinine >1.5xULN: HR=1.55, P=.001; CrCl<60ml/min: HR=1.37, P=.01), and cardiac dysfunction (QTc >480-500ms: HR=1.36, P=.04; LVEF 50-40%: HR=1.38, P=.04) were, however, associated with worse survival. Conclusions: Age, comorbidities, and organ dysfunction are associated with AML treatment determination. Pts who presented with cancer/renal/myocardial comorbidities and renal/liver dysfunction were more likely to receive NIC, despite the comorbid conditions not having an impact on OS. Further research needs to elucidate the extent to which NIC is the optimal treatment decision for pts exhibiting varying severities of cancer/renal/myocardial comorbidities. Disclosures Hobbs: SimulStat Inc.: Consultancy; Amgen: Research Funding. Mukherjee:McGraw Hill Hematology Oncology Board Review: Other: Editor; Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Projects in Knowledge: Honoraria; Bristol-Myers Squibb: Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria. Advani:Glycomimetics: Consultancy, Research Funding; Amgen: Research Funding; Kite Pharmaceuticals: Consultancy; Macrogenics: Research Funding; Abbvie: Research Funding; Pfizer: Honoraria, Research Funding. Gerds:Sierra Oncology: Research Funding; Celgene Corporation: Consultancy, Research Funding; Pfizer: Consultancy; Incyte: Consultancy, Research Funding; CTI Biopharma: Consultancy, Research Funding; Imago Biosciences: Research Funding; Roche: Research Funding. Nazha:Daiichi Sankyo: Consultancy; Tolero, Karyopharma: Honoraria; MEI: Other: Data monitoring Committee; Novartis: Speakers Bureau; Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; Abbvie: Consultancy. Sekeres:Millenium: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees.

Pattern of Somatic Mutation Changes from Diagnosis to Relapse Following Allogeneic Hematopoietic Cell Transplantation (alloHCT) for Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS)

Pattern of Somatic Mutation Changes from Diagnosis to Relapse Following Allogeneic Hematopoietic Cell Transplantation (alloHCT) for Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS)

ANKRD26 Coding Variants Presenting with Giant Platelets and a Predisposition to Myeloid Neoplasia

ANKRD26 Coding Variants Presenting with Giant Platelets and a Predisposition to Myeloid Neoplasia

Identifying Factors That Predict for Unplanned Readmissions for Acute Myeloid Leukemia Patients Receiving Consolidation Cytarabine Based Therapies

Background: Younger patients (pts) with acute myeloid leukemia (AML) who enter a remission after intensive induction chemotherapy routinely receive at least one cycle of consolidation therapy with high dose cytarabine (HiDAC). This is commonly administered over a five-day inpatient stay, after which pts are discharged home as their blood counts nadir. It is thus a natural consequence of therapy that readmission for febrile neutropenia (FN) occurs, which can impact measures of quality and value in this population. Precise descriptions of incidence, type, and severity of infection, if identified, are lacking, and thus it is unknown to what standard cancer centers should be held for anticipated readmission. We measured these rates, and attempted to identify predictive factors for readmission. Methods: Adult AML pts ≥ 18 years of age who received at least one cycle of HiDAC consolidation (1000-3000 mg/m2 for six doses) in 2009-2019 were included. Our primary aim was to identify predictive factors for readmission after the first cycle of consolidation chemotherapy. The following pt characteristics and co-morbid conditions were analyzed: age, gender, body mass index (BMI), smoking status, AML cytogenetic risk status, history of diabetes, peripheral vascular disease, cardiovascular disease, chronic pulmonary disease, hepatic impairment, and other cancers. Secondary aims included: estimating rates of all-cause readmissions among all HiDAC cycles, defining the rate of FN readmissions, estimating rates of intensive care unit (ICU) admissions, clinical (e.g., probable pneumonia per imaging) and microbiologically-documented infections, prophylactic (ppx) medications used, and mortality. Statistical analyses interrogated potential risk factors for evidence of association with hospital readmission after the first cycle of consolidation chemotherapy. Results: We identified 182 AML pts who fit inclusion criteria. The median age was 50 years (range 19-73); 55% were female and 45% were male. Statistical analyses revealed no association with readmission after cycle 1 for cytogenetic risk (p=0.85), history of heart failure (p= 0.67), chronic pulmonary disease (p=1), connective tissue disease (p=0.53), cerebrovascular accident (p=0.63), diabetes (p=0.63), gender (p=0.07), history of lymphoma (p=0.53), other solid tumors (p=0.53), liver disease (p=1), myocardial infarction (p=0.71), peripheral vascular disease (p=1), or smoking status (p= 0.52). For 480 HiDAC cycles analyzed (88% at 3000 mg/m2), the overall readmission rate was 50% (242/480), of which 85% (205/242) were for FN. Those readmissions which were not FN were for cardiac complications (chest pain, EKG changes), non-neutropenic fevers or infections, neurotoxicity, bleeding or clotting events, or other symptoms associated with chemotherapy (nausea/vomiting, pain, etc.). Median time to FN hospital admission was 18 days (range 6-27) from the start of HiDAC. Of the 205 FN readmissions, 57% had documented infections. Of these infections, 41% were bacteremia, 23% fungal, 16% sepsis, 12% other bacterial, and 8% viral. Of 480 HiDAC cycles, ppx medications prescribed included: 92% fluoroquinolone (442/480), 81% anti-viral (389/480), 30 % anti-fungal (142/480), and 3% colony stimulating factor (14/480). Only 7% (14/205) of FN readmissions resulted in an ICU admission, and 1% (3/205) resulted in death. Conclusions: Approximately half of patients treated with consolidation therapy following intensive induction therapy can be expected to be readmitted to the hospital. The majority of FN readmissions were associated with clinical or microbiologically documented infections and are not avoidable, however ICU admission and death associated with these complications are rare. Readmission of AML pts following HiDAC is expected, and therefore, should be excluded from measures of value and quality. Disclosures Waldron: Amgen: Consultancy. Hobbs:Amgen: Research Funding; SimulStat Inc.: Consultancy. Advani:Macrogenics: Research Funding; Abbvie: Research Funding; Kite Pharmaceuticals: Consultancy; Pfizer: Honoraria, Research Funding; Amgen: Research Funding; Glycomimetics: Consultancy, Research Funding. Nazha:Incyte: Speakers Bureau; Abbvie: Consultancy; Daiichi Sankyo: Consultancy; Jazz Pharmacutical: Research Funding; Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee; Tolero, Karyopharma: Honoraria. Gerds:Imago Biosciences: Research Funding; Roche: Research Funding; Celgene Corporation: Consultancy, Research Funding; Pfizer: Consultancy; CTI Biopharma: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Sierra Oncology: Research Funding. Sekeres:Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium: Membership on an entity's Board of Directors or advisory committees. Mukherjee:Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; McGraw Hill Hematology Oncology Board Review: Other: Editor; Projects in Knowledge: Honoraria; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees.

Real-World Treatment Patterns and Transfusion Burden Among US Patients with Newly Diagnosed Myelodysplastic Syndromes (MDS)

Introduction: Symptomatic anemia transfusion support represents a significant problem for patients with MDS. The principal strategy in managing these patients remains supportive care with erythropoiesis-stimulating agents (ESAs) and red blood cell (RBC) transfusions. However, there is paucity of real-world data regarding patterns of use of ESA and other therapies, and its impact on transfusion needs in these patients early in their clinical course. To address this question, we performed a retrospective claims-based study to characterize treatment patterns and transfusion burden in patients with MDS. Methods: In this retrospective claims analysis of a large national health insurance plan, all patients aged ≥ 18 years with newly diagnosed MDS (≥ 2 medical claims with an International Classification of Diseases, 9th or 10th Revision [ICD-9 or ICD-10] diagnosis codes ≥ 30 days apart) identified between January 2012 and May 2018 were included. Index date was defined as date of first diagnosis. Continuous health plan enrollment for ≥ 6 months pre- and 3 months post-index date was required. RBC transfusion status was evaluated during the 16 weeks prior to first diagnosis as well as 16 weeks prior to and immediately following line of therapy (LOT) 1 and LOT2. Transfusion burden categories were adapted and modified from the proposed International Working Group 2018 revised criteria (Platzbecker U, et al., Blood. 2019;133(10):1096-1107). Categories included transfusion independence (TI), defined as patients receiving no transfusions during the observation period; low (LTB), moderate (MTB), and high transfusion burden (HTB) were defined by patient's having 1-3, 4-7, and ≥ 8 unique dates for a transfusion during the observation periods, respectively. Therapies in each LOT were captured using pharmacy and medical claims data. The end of a LOT was defined after ≥ 60-day gap in therapy, a claim for any new or additional MDS therapy, or patient death; LOT durations are described for non-censored patients. Results: Among the 3,587 patients identified (mean age = 74.9 years, 44.3% female), transfusion burden during 16 weeks prior to index was: 78.8% TI, 19.2% LTB, 1.9% MTB, and 0.2% HTB. Among the 1,935 patients who received LOT1, transfusion burden in the 16 weeks preceding LOT1 was: 57.0% TI, 36.3% LTB, 5.6% MTB, and 1.1% HTB. The top 5 regimens in LOT1 were ESA monotherapy (61.9%), hypomethylating agent (HMA) monotherapy (19.2%), white blood cell growth factor (WBCGF) monotherapy (3.5%), immunomodulators (3.3%), and HMA + ESA (2.7%) (Figure). Of 824 patients who received LOT2, transfusion burden prior to LOT2 was: 49.4% TI, 28.6% LTB, 16.5% MTB, and 5.5% HTB. The top 5 regimens in LOT2 were ESA monotherapy (44.2%), HMA monotherapy (12.1%), HMA + ESA (9.2%), WBCGF + ESA (6.9%), and WBCGF monotherapy (6.4%) (Figure). In LOT1, the median treatment duration for ESA monotherapy was 2.8 months (mean = 5.2 months, standard deviation [SD] = 6.8) whereas in LOT2, the median treatment duration was 2.0 months (mean = 3.7 months, SD = 5.0). Amongst patients receiving ESA monotherapy as LOT2, 90.4% had prior ESA monotherapy as LOT1. In patients treated with HMA monotherapy in LOT1 that also experienced a LOT2, 39.1% moved on to a variety of LOT2 regimens (Table), while in 15.5% ESAs were combined with HMA (Table). Conclusions: Our results show that at the time of diagnosis, 20% of MDS patients were transfusion dependent, but up to 50% require treatment. The high rate of ESA use is likely due to anemia-related symptoms. In those treated with ESA monotherapy, approximately 50% have a LOT1 duration that is < 3 months, and interestingly, the majority of patients restart a second LOT with an ESA as the most common regimen. Additional analyses are necessary to determine whether this indicates a switch in ESA agent, escalation in dose of prior ESA, treatment cycling due to elevated hemoglobin, or other reasons. Considering the short median treatment duration and worsening of transfusion dependency beyond first line, there remains a high unmet need for MDS therapy that more effectively slows the progression of transfusion dependence. Disclosures Mukherjee: Bristol-Myers Squibb: Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Projects in Knowledge: Honoraria; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; McGraw Hill Hematology Oncology Board Review: Other: Editor. Slabaugh:Celgene Corporation: Employment, Equity Ownership. Copher:Celgene Corporation: Employment. Johnson:Optum: Employment; Celgene Corporation: Consultancy. Buzinec:Optum: Employment. Mearns:Celgene Corporation: Employment.