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Myelodysplastic syndromes (MDSs) are potentially devastating monoclonal deviations of hematopoiesis that lead to bone marrow dysplasia and variable cytopenias. Predicting severity of disease progression and likelihood to undergo acute myeloid leukemia transformation is the basis of treatment strategy. Some patients belong to a low-risk cohort best managed with conservative supportive care, whereas others are included in a high-risk cohort that requires decisive therapy with hematopoietic cell transplantation or hypomethylating agent administration. Risk scoring systems for MDS prognostication were traditionally based on karyotype characteristics and clinical factors readily available from chart review, and validation was typically conducted on de novo MDS patients. However, retrospective analysis found a large subset of patients incorrectly risk-stratified. In this review, the most commonly used scoring systems are evaluated, and pitfalls therein are identified. Emerging technologies such as personal genomics and machine learning are then explored for efficacy in MDS risk modeling. Barriers to clinical adoption of artificial intelligence-derived models are discussed, with focus on approaches meant to increase model interpretability and clinical relevance. Finally, a guiding set of recommendations is proposed for best designing an accurate and universally applicable prognostic model for MDS, which is supported by more than 20 years of observation of traditional scoring system performance, as well as modern efforts in creating hybrid genomic-clinical scoring systems.

PURPOSE Patients with myelodysplastic syndromes (MDS) have a survival that can range from months to decades. Prognostic systems that incorporate advanced analytics of clinical, pathologic, and molecular data have the potential to more accurately and dynamically predict survival in patients receiving various therapies. METHODS A total of 1,471 MDS patients with comprehensively annotated clinical and molecular data were included in a training cohort and analyzed using machine learning techniques. A random survival algorithm was used to build a prognostic model, which was then validated in external cohorts. The accuracy of the proposed model, compared with other established models, was assessed using a concordance (c)index. RESULTS The median age for the training cohort was 71 years. Commonly mutated genes included SF3B1, TET2, and ASXL1. The algorithm identified chromosomal karyotype, platelet, hemoglobin levels, bone marrow blast percentage, age, other clinical variables, seven discrete gene mutations, and mutation number as having prognostic impact on overall and leukemia-free survivals. The model was validated in an independent external cohort of 465 patients, a cohort of patients with MDS treated in a prospective clinical trial, a cohort of patients with paired samples at different time points during the disease course, and a cohort of patients who underwent hematopoietic stem-cell transplantation. CONCLUSION A personalized prediction model on the basis of clinical and genomic data outperformed established prognostic models in MDS. The new model was dynamic, predicting survival and leukemia transformation probabilities at different time points that are unique for a given patient, and can upstage and downstage patients into more appropriate risk categories.

Background While histo- and cytomorphological examinations are central to the diagnosis of myelodysplastic syndromes (MDS), significant inter-observer variability exists. The diagnosis can be challenging in pancytopenic patients (pts) without evidence of dysplasia and is contingent on observer expertise. We developed and externally validated a geno-clinical model that uses mutational data and peripheral blood counts/clinical variables to distinguish MDS from other myeloid malignancies. Methods Clinical and genomic data, including commercially available next-generation sequencing panels, were obtained for patients (pts) treated at the Cleveland Clinic (CC; 652 pts), Munich Leukemia Laboratory (MLL; 1509 pts), and the University of Pavia in Italy (UP, 536 pts). All patients had carried a diagnosis of MDS, chronic myelomonocytic leukemia (CMML), MDS/myeloproliferative neoplasm overlap (MDS/MPN), myeloproliferative neoplasm (MPN; either polycythemia vera, essential thrombocythemia, or myelofibrosis), clonal cytopenia of undetermined significance (CCUS), or idiopathic cytopenia of undetermined significance (ICUS). All diagnoses were established with bone marrow aspiration and according to World Health Organization 2017 criteria. The training cohort included data from CC and UP and randomly divided into learner (80%) and test (20%) cohorts. The final model was independently validated in the MLL cohort. A machine learning algorithm was used to build the model; multiple extraction algorithms were used to extract genomic/clinical variables on both the cohort and individual levels. Performance was evaluated according to the area under the curve of the receiver operating characteristic (ROC-AUC) and accuracy matrices. Results Among the 2697 pts included from all sites, the median age was 70 years [36 - 86]. Median hemoglobin (Hb) was 10.4g/dl [6.9 - 15.7], median platelet count (PLT) was 132 k/dL [14 - 722], median WBC count was 5.3 k/dL [1.4 - 49.9], median ANC was 2.8 k/dL [0.3 - 27.7], median monocyte count was 0.3 k/dL [0 - 9.9], and median lymphocyte count (ALC) was 1.1 k/dL [0.1 - 5.4], and median peripheral blast percentage 0% [0 - 8]. The most commonly mutated genes in all patients were (list top 5 genes) and among pts with MDS were SF3B1 (27%), TET2 (25%), ASXL1 (19%), SRSF2 (16%), and DNMT3A (11%); among patients with MDS-MPN/CMML, the most commonly mutated genes were MDS-MPN/CMML (TET2 46%, ASXL1 34%, SRSF2 29%, RUNX1 13%, CBL 12%) ; among patients with MPNs, the most commonly mutated genes were (JAK2 64%, ASXL1 27%, TET2 14%, DNMT3A 8%, U2AF1 7%); among patients with CCUS the most commonly mutated genes were (TET2 41%, DNMT3A 27%, ASXL1 19%, SRSF2 17%, ZRSR2 10%). The most important features for model predictions (ranked from the most to the least important) included: number of mutations detected/sample, peripheral blast percentage, AMC, JAK2 status, Hb, basophil count, age, eosinophil count, ALC, WBC, EZH2 mutation status, ANC, mutation status of KRAS and SF3B1, platelets, and gender. The final model achieved an average AUROC of 0.95 (95% CI 0.93-0.96) when applied to the test cohort and 0.93 (95% CI 0.91 - 0.94) when it was applied to the MLL cohort. The model also provides individual-level explanations for predictions, providing top differential diagnoses and individual-level explanations of how features influence a putative diagnosis (Figure 1b). Conclusions We developed and externally validated a highly accurate and interpretable model that can distinguish MDS from other myeloid malignancies using clinical and mutational data from a large international cohort. The model can provide personalized interpretations of its outcome and can aid physicians and hematopathologists in recognizing MDS with high accuracy when encountering pts with pancytopenia and with a suspected diagnosis of MDS. Disclosures Sekeres: Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda/Millenium: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees. Mukherjee:Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Honoraria; Bristol Myers Squib: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Aplastic Anemia and MDS International Foundation: Honoraria; Celgene/Acceleron: Membership on an entity's Board of Directors or advisory committees; EUSA Pharma: Consultancy. Gerds:Sierra Oncology: Research Funding; Imago Biosciences: Research Funding; Apexx Oncology: Consultancy; Celgene: Consultancy, Research Funding; Incyte Corporation: Consultancy, Research Funding; Roche/Genentech: Research Funding; CTI Biopharma: Consultancy, Research Funding; AstraZeneca/MedImmune: Consultancy; Gilead Sciences: Research Funding; Pfizer: Research Funding. Maciejewski:Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria. Nazha:Jazz: Research Funding; Incyte: Speakers Bureau; Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee.

Myelodysplastic syndromes are disorders of clonal myelopoiesis having a range of clinical manifestations, from benign and indolent to aggressive with very poor prognosis. Classifying the likely trajectory of disease within a patient largely guides therapeutic decision making and therefore survival. Traditional methods of risk-stratification systems rely on clinical features: simple blood tests, peripheral smears, bone marrow biopsies, and cytogenetics, but do not adequately predict disease severity for a substantial proportion of patients. This article reviews the state of stratification at use in the clinic, describes emerging systems that leverage large-scale genomic data, and summarizes efforts toward truly personalized prediction models.

Background: Myelodysplastic syndromes (MDS) and related myeloid malignancies are highly variable in both their clinical manifestations and underlying genetic abnormalities. While few mutations in myeloid malignancies are considered disease-defining, significant and complex associations between these genes exist and can influence the clinical characteristics and disease phenotype. Here, we took advantage of a large, international cohort of patients with myeloid malignancies to define genotype-phenotype relationships using state of the art machine learning models. Methods Data were collected for patients (pts) from the Cleveland Clinic (CC; 652 pts), Munich Leukemia Laboratory (MLL; 1509 pts), and the University of Pavia in Italy (UP; 536 patients). Clinical data including CBC at time of diagnosis and a genomic panel of 20 commonly mutated genes in myeloid malignancies were analyzed. Gene-gene correlations within disease subtypes, individual genes' co-occurrence or exclusivity within disease subtypes, and the co-occurrence or exclusivity of individual genes with clinically meaningful features including karyotypic abnormalities and severe cytopenias (defined as hemoglobin < 8 g/dL, platelets < 50k/dL, and ANC < 1k/dL) were evaluated using multiple machine learning/correlation/feature extraction algorithms. Results 2697 pts were included, 1630 (60%) with MDS, 399 (15%) with chronic myelomonocytic leukemia (CMML), 142 (5%) with idiopathic cytopenia of undetermined significance (ICUS), 129 (5%) with MDS-MPN overlap syndromes (MDS-MPN), 95 (4%) with primary myelofibrosis (PMF), 93 (3%) with clonal cytopenia of undetermined significance (CCUS), 52 (2%) with essential thrombocythemia (ET), 41 (2%) with polycythemia vera (PV), and 26 (1%) with other myeloproliferative neoplasms (MPNs). The median age at diagnosis for the entire cohort was 70 years [36 - 86]. Of patients with karyotype data available, 1091 pts (50%) had a normal karyotype, 17 (1%) had chromosome 17 abnormalities, 96 (4%) had chromosome 7 abnormalities, 145 (7%) had chromosome 5 abnormalities, and 123 (6%) had a complex karyotype. The most commonly mutated genes were: TET2 (28%), ASXL1 (22%), SF3B1 (22%), SRSF2 (19), JAK2 (11%), DNMT3A (9%), RUNX1 (9%), and U2AF1 (6%) SF3B1 mutations were associated with normal karyotype (NK), age 1 k/dL, platelets (plts) >50 k/dL, marrow blasts (MB) Clinical characteristics were also associated with specific genomic alterations (Figure 1). For example, NK correlated with the presence of SF3B1, ZRSR2, DNMT3A, a higher number of mutations, and absence of TP53, ASXL1, or KRAS; chromosome 5, 7, and 17 abnormalities were associated with a lower mutation number and the presence of TP53 mutations; complex karyotype correlated with the absence of TET2 and SF3B1 and the presence of TP53; age < 65 was associated with the presence of NRAS and JAK2 mutations and the absence of TET2, SF3B1, and SRSF2 mutations; hemoglobin < 8 g/dL positively correlated with mutation number and SF3B1 mutations and negatively correlated with TET2 mutations; ANC < 1 negatively correlated with JAK2, SF3B1, and DNMT3A mutations; platelets < 50k/dL negatively correlated with SF3B1 and JAK2 mutations, and positively correlated with the number of mutations; and MB Conclusions We applied machine learning techniques to reveal the complex relationships between mutational data and the clinical characteristics of several myeloid malignancies using a large, international patient cohort. In addition to correctly identifying previously described genotype-phenotype relationships, we identified several other intriguing relationships such as the relationship of particular mutations to the development of different cytopenias, demonstrating the potential utility for machine learning approaches in interrogating genomic data. Disclosures Sekeres: BMS: Consultancy; Pfizer: Consultancy; Takeda/Millenium: Consultancy. Gerds:Gilead Sciences: Research Funding; Imago Biosciences: Research Funding; CTI Biopharma: Consultancy, Research Funding; Pfizer: Research Funding; Sierra Oncology: Research Funding; AstraZeneca/MedImmune: Consultancy; Incyte Corporation: Consultancy, Research Funding; Apexx Oncology: Consultancy; Celgene: Consultancy, Research Funding; Roche/Genentech: Research Funding. Mukherjee:Aplastic Anemia and MDS International Foundation: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers Squib: Honoraria; Partnership for Health Analytic Research, LLC (PHAR, LLC): Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; EUSA Pharma: Consultancy; Celgene/Acceleron: Membership on an entity's Board of Directors or advisory committees. Maciejewski:Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria. Nazha:Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee; Jazz: Research Funding; Incyte: Speakers Bureau.

The emphasis on fibrinolysis as an important contributor to trauma-induced coagulopathy (TIC) has led to a debate regarding the relative clinical significance of fibrinolysis in the setting of trauma. The debate has centered on two camps. The one camp defines fibrinolysis in trauma by standard coagulation tests as well as fibrin split products, D-dimers, and plasmin/antiplasmin levels. This camp favors a more liberal use of tranexamic acid and attributes more significance to hyperfibrinolysis in TIC. The other camp favors a definition of fibrinolysis based on the viscoelastic tests (VET), rotational thromboelastometry (ROTEM), and thromboelastography (TEG). These whole blood assays define hyperfibrinolysis at a higher threshold than plasma-based tests. Therefore, this VET camp reserves antifibrinolytic treatment for patients who demonstrate severe coagulopathy associated with hyperfibrinolysis. This bimodal attribution of the clinical relevance of fibrinolysis in trauma suggests that there may be an underlying "Myth" of the concept of TIC that was historically defined by plasma-based tests and a future "Reality" of the concept of TIC that is grounded on an understanding of TIC based on a VET-defined "fibrinolytic spectrum" of TIC. This narrative review explores this "Myth" and "Reality" of fibrinolysis in TIC and proposes a direction that will allow a "Future" interpretation of TIC that incorporates both the past "Myth" and present "Reality" of fibrinolysis TIC.

7057 Background: Cancer patients (pts) are at high risk of unplanned hospital readmissions. Predicting which cancer patients are at higher risk of readmission would improve post-discharge follow-up/navigation, decrease cost, and improve pt outcomes. Methods: We conducted a retrospective cohort study of non-surgical cancer pts hospitalized at our center between 12/2014 to 7/2018. A machine learning algorithm was trained on 348 medical, sociodemographic and cancer-specific variables with a total of 1,801,944 data points. The cohort was randomly divided into training (80%) and validation (20%) subsets. Prediction performance was measured by area under the receiver operator characteristic curve (AUC). Results: A total of 5,178 hospitalizations were included, of which 45.1% were women, and 27.6% experienced an unplanned readmission within 30 days. The most frequently represented cancers were hematologic malignancies (30.5%), followed by GI (18.1%), lung (13.7%), and GU (10.9%). Significant variables that impacted the algorithm decision are ranked from the most to the least important, including: days from last admission; planned index chemotherapy admission; number of vascular access lines, drains, and airways in use; length of stay; cancer diagnosis; total ED visits in past 6 months; age; discharge lab values (sodium, albumin, alkaline phosphatase, bilirubin, platelets); number of prior admissions; and discharge disposition. The AUC for the validation subset was 0.80. To ease the translation of this model into the clinic, we developed a web application whereby users can supply the aforementioned variables to the model and receive a personalized prediction that highlights those variables most affecting a subject’s readmission risk status: www.Cancer-Readmission.com. Conclusions: A cancer-specific readmission risk model with high AUC for 30-days unplanned readmission has been developed. The model is embedded in a freely available web application that provides personalized, patient-specific predictions. Programs that integrate this model can identify cancer patients with a greater risk for unplanned hospital readmission, thus providing a personalized approach to prevent future unplanned readmissions.

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.

Background Myelodysplastic syndromes (MDS) and other myeloid neoplasms are mainly diagnosed based on morphological changes in the bone marrow. Diagnosis can be challenging in patients (pts) with pancytopenia with minimal dysplasia, and is subject to inter-observer variability, with up to 40% disagreement in diagnosis (Zhang, ASH 2018). Somatic mutations can be identified in all myeloid neoplasms, but no gene or set of genes are diagnostic for each disease phenotype. We developed a geno-clinical model that uses mutational data, peripheral blood values, and clinical variables to distinguish among several bone marrow disorders that include: MDS, idiopathic cytopenia of undetermined significance (ICUS), clonal cytopenia of undetermined significance (CCUS), MDS/myeloproliferative neoplasm (MPN) overlaps including chronic myelomonocytic leukemia (CMML), and MPNs such as polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (PMF). Methods We combined genomic and clinical data from 2471 pts treated at our institution (684) and the Munich Leukemia Laboratory (1787). Pts were diagnosed with MDS, ICUS, CCUS, CMML, MDS/MPN, PV, ET, and PMF according to 2016 WHO criteria. Diagnoses were confirmed by independent hematopathologists not associated with the study. A panel of 60 genes commonly mutated in myeloid malignancies was included. The cohort was randomly divided into learner (80%) and validation (20%) cohorts. Machine learning algorithms were applied to predict the phenotype. Feature extraction algorithms were used to extract genomic/clinical variables that impacted the algorithm decision and to visualize the impact of each variable on phenotype. Prediction performance was evaluated according to the area under the curve of the receiver operator characteristic (ROC-AUC). Results Of 2471 pts, 1306 had MDS, 223 had ICUS, 107 had CCUS, 478 had CMML, 89 had MDS/MPN, 79 had PV, 90 had ET, and 99 had PMF. The median age for the entire cohort was 71 years (range, 9-102); 38% were female. The median white blood cell count (WBC) was 3.2x10^9/L (range, 0.00-179), absolute monocyte count (AMC) 0.21x10^9/L (range, 0-96), absolute lymphocyte count (ALC) 0.88x10^9/L (range, 0-357), absolute neutrophil count (ANC) 0.60x10^9/L (range, 0-170), and hemoglobin (Hgb) 10.50 g/dL (range, 3.9-24.0). The most commonly mutated genes in all pts were: TET2 (28%), ASXL1 (23%), SF3B1 (15%). In MDS, they were: TET2 (26%), SF3B1 (24%), ASXL1 (21%). In CCUS: TET2 (46%), SRSF2 (24%), ASXL1 (23%). In CMML, TET2 (51%), ASXL1 (43 %), SRSF2 (25%). In MDS/MPN: SF3B1 (39%), JAK2 (37%), TET2 (20%). In PV, JAK2 (94%), TET2 (22%), DNMT3A (8%). In ET: JAK2 (44%), TET2 (13%), DNMT3A (8%). In PMF: JAK2 (67%), ASXL1 (43%), SRSF2 (17%). 71 genomic/clinical variables were evaluated. Feature extraction algorithms were used to identify the variables with the most significant impacts on prediction. The top variables are shown in the Figure 1. Overall, the most important variables were: age, AMC, ANC, Hgb, Plt, ALC, total number of mutations, JAK2, ASXL1, TET2, U2AF1, SRSF2, SF3B1, BCOR, EZH2, and DNMT3A. The top variables for each disease were different, see Figure. When applying the model to the validation cohort, AUC performance was as follows (a perfect predictor has an AUC of 1, and AUC ≥ 0.90 are generally considered excellent): MDS: 0.95 +/- 0.04, ICUS: 0.96 +/- 0.05, CCUS: 0.95 +/- 0.05, CMML: 0.95 +/- 0.05, MDS/MPN: 0.95 +/- 0.05, PV: 0.95 +/- 0.05, ET: 0.96 +/- 0.05, PMF: 0.95 +/- 0.05. When the analysis was restricted to MDS, ICUS, and CCUS, the AUC remained high, 0.95 +/- 0.4. The model can also provide personalized explanations of the variables supporting the prediction and the impact of each variable on the outcome (Figure). Conclusions We propose a new approach using interpretable, individualized modeling to predict myeloid neoplasm phenotypes based on genomic and clinical data without bone marrow biopsy data. This approach can aid clinicians and hematopathologists when encountering pts with cytopenias and suspicion for these disorders. The model also provides feature attributions that allow for quantitative understanding of the complex interplay among genotypes, clinical variables, and phenotypes. A web application to facilitate the translation of this model into the clinic is under development and will be presented at the meeting. Figure 1 Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. 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. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Savona:Incyte Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Selvita: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; TG Therapeutics: 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, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Patents & Royalties; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees. Gerds:Incyte: Consultancy, Research Funding; Roche: Research Funding; Imago Biosciences: Research Funding; CTI Biopharma: Consultancy, Research Funding; Pfizer: Consultancy; Celgene Corporation: Consultancy, Research Funding; Sierra Oncology: Research Funding. Mukherjee: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; Pfizer: Honoraria; Bristol-Myers Squibb: Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees. Komrokji:JAZZ: Speakers Bureau; Agios: Consultancy; Incyte: Consultancy; DSI: Consultancy; pfizer: Consultancy; celgene: Consultancy; JAZZ: Consultancy; Novartis: Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Alexion: Consultancy; Novartis: Consultancy. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:Tolero, Karyopharma: Honoraria; MEI: Other: Data monitoring Committee; Novartis: Speakers Bureau; Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; Daiichi Sankyo: Consultancy; Abbvie: Consultancy.

Background AML is a heterogeneous clonal disorder that is characterized by the accumulation of complex genomic alterations that affect disease biology and outcomes. Despite significant advances in our understanding of the impact of these mutations on overall survival (OS), established AML risk stratification guidelines are based primarily on cytogenetic analyses and a limited number of genes, don't take into account the complexity and the interaction between these mutations, and how particular constellations of genomic and clinical risk factors affect patient (pt) outcome. We developed a novel prognostic model that incorporates clinical, cytogenetic, and mutational data to determine personalized outcomes specific to a particular pt. Method A total of 792,779 genomic and clinical data points from 3,421 pts were analyzed. The cohort was comprised of five independent datasets: 443 pts from the Beat AML Master Trial (Tyner et al, Nature, 2018), 855 pts from Cleveland Clinic, 414 pts from Munich Leukemia Laboratory (MLL), 1,509 pts from the German-Austrian Study Group (Papaemmanuil et al, NEJM, 2016), and 200 pts from The Cancer Genome Atlas (NEJM, 2013). A panel of 44 gene mutations commonly implicated in AML was used in the analysis, along with numerous cytogenetic and clinical variables such age, white blood cell count WBC) at diagnosis, and AML subtype (primary vs. secondary vs. therapy-related. A machine learning algorithm capable of accounting for survival (XGBOOST) was used to build the new model, in which clinical and molecular variables were randomly selected for inclusion in determining OS. Feature extraction algorithms were used to isolate the most important variables that impacted decision making within the model. The algorithm can also plot the important features that are specific for a given pt and show the impact of each feature on the outcome (positive vs. negative). The C-index was used to evaluate the accuracy of the new model compared to 2017 ELN risk classification. Results The median age of the cohort was 56 years (range, 18-100); 1,122 pts (32.8%) had favorable risk cytogenetics per ELN criteria, 956 (27.9%) intermediate (INT), and 1,343 (39.3%) adverse. The most commonly mutated genes were: NPM1 (24%), FLT3 (23%), DNMT3A (20%), NRAS (13%), IDH2 (11%), RUNX1 (10%) and TET2 (10%). Mutations occurred in different frequencies in each cytogenetic risk group. The most commonly mutated genes in the favorable risk group were: NRAS (30%), KIT (23%), FLT3 (17%), and KRAS (8%). The most commonly mutated genes in the INT risk group were: NPM1 (28%), FLT3 (26%), DNMT3A (22%), IDH2 (12%), TET2 (11%), NRAS (11%), and RUNX1 (11%). The most commonly mutated genes in pts with adverse cytogenetics included: TP53 (34%), DNMT3A (13%), NRAS (11%), RUNX1 (10%), PTPN11 (8%), IDH2 (7%), U2AF1 (6%) and FLT3 (6%). All genomic-clinical variables were included in the machine learning algorithm. Variable importance analyses (the most important variables that contributed to the outcome) and multiple backward elimination analyses (identifying the least number of variables that can provide the least error rate) identified the following variables that impacted OS: age, transplant (yes vs. no), WBC, bone marrow blast %, cytogenetics, ASXL1, CEBPA, DNMT3A, FLT3, KDM6A, KIT, KRAS, NPM1, NRAS, PHF6, PTPN11, RUNX1, TET2, and TP53. The clinical and mutational variables that impacted each pt outcome can be visualized in a highly personalized manner, Figure 1. The C-index for the new model was 0.80 which significantly outperformed ELN classification (0.59). When applying the new model to each of the five patient cohorts, the c-indices remained high and were as follows: Beat AML (0.81), Cleveland Clinic (0.85), MLL (0.83), Papaemmanuil E, et al (0.79), and TCGA (0.80). Conclusions Genomic alterations have a differential impact on OS in each cytogenetic risk group, highlighting the complexity of incorporating these mutations into risk stratification. A personalized prediction model based on clinical-genomic data can accurately provide survival unique to each individual pt and can significantly outperform ELN classifications or any currently available models. To ease the translation of this model into the clinic, a web application is currently under development and will be publicly available for use. Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. 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. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Maciejewski:Novartis: Consultancy; Alexion: Consultancy. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. 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. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:Jazz Pharmacutical: Research Funding; Novartis: Speakers Bureau; Incyte: Speakers Bureau; Tolero, Karyopharma: Honoraria; Abbvie: Consultancy; Daiichi Sankyo: Consultancy; MEI: Other: Data monitoring Committee.