Introduction: AML is a heterogeneous disease with diverse patient outcomes. During the last 4 decades, several cytogenetic and molecular markers have been used for risk stratification of AML pts and to guide therapeutic decisions. In 2017, Gerstung et al. (Nat Genet 2017;49:332) used a knowledge bank (KB; i.e., combination of clinical, outcome, cytogenetic and sequencing data of 111 genes from 1,540 AML pts) to generate an algorithm that is able to predict likelihoods for remission, relapse, and mortality in AML pts. The prognostic value of the established KB algorithm was validated in two independent but smaller pt cohorts (Gerstung et al.: n=186 pts; Huet et al. Blood 2018;132:865: n=155 pts). Aims: The aim of our study was to validate the prognostic relevance of the KB algorithm both in our entire large independent adult pt cohort and in age subgroups (i.e., younger [ Methods: We analyzed 1,617 pts (median age: 53 y; 1,048 aged Results: We used the 3-y overall survival (OS) rates to compare the KB algorithm prediction with the actual outcome. In the whole cohort, we found the area under the receiver operating characteristic curve (AUC) to be AUCKB = 0.79 (Figure 1A). Of note, AUC = 1.00 means perfect prediction ability whereas AUC = 0.50 denotes lack of prediction ability equal to that of random chance. Concerning other clinical endpoints, we found that the KB approach had the highest AUC for predicting non-remission death (i.e., pts who died without achieving a CR1, AUCKB = 0.84), followed by relapse death (i.e., pts who died after relapse, AUCKB = 0.69), and non-relapse death (i.e., pts who died in CR1, AUCKB = 0.61). Analysis of the 3-y OS in the subgroup of younger pts yielded similar results with an AUCKB = 0.78. Older pts are known to have poorer prognosis and risk stratification is more difficult for this cohort, but, we still found an AUCKB = 0.79 for the KB approach. Next, we compared the predictive value of the KB approach with the current ELN classification and found that KB outperformed the ELN classification in the whole cohort (AUCKB = 0.79 vs AUCELN = 0.53, P Conclusions: Our analysis of a large cohort of 1,617 pts with de novo AML treated with intensive chemotherapy validated the prognostic value of the recently published KB algorithm for the 3-y OS endpoint. Although we found that the KB approach had a high predictive relevance for non-remission death, the AUCs for relapse death and non-relapse death were lower. We also showed that the KB approach had a better predictive value than the current ELN classification but the differences in the AUCs were smaller when ED pts were excluded. Support: CA233338, U24CA196171, U10CA180821, U10CA180882. https://acknowledgments.alliancefound.org Disclosures Kolitz: Boeringer-Ingelheim: Research Funding; Astellas: Research Funding; Roche: Research Funding. Powell:Jazz Pharmaceuticals: Consultancy, Research Funding, Speakers Bureau; Pfizer: Consultancy, Research Funding; Rafael Pharmaceuticals: Consultancy, Research Funding; Novartis: Consultancy, Speakers Bureau; Janssen: Research Funding. Stone:AbbVie, Actinium, Agios, Argenx, Arog, Astellas, AstraZeneca, Biolinerx, Celgene, Cornerstone Biopharma, Fujifilm, Jazz Pharmaceuticals, Amgen, Ono, Orsenix, Otsuka, Merck, Novartis, Pfizer, Sumitomo, Trovagene: Consultancy; Argenx, Celgene, Takeda Oncology: Other: Data and Safety Monitoring Board/Committee: ; Novartis, Agios, Arog: Research Funding. Byrd:Janssen: Consultancy, Other: Travel Expenses, Research Funding, Speakers Bureau; Gilead: Other: Travel Expenses, Research Funding, Speakers Bureau; Ohio State University: Patents & Royalties: OSU-2S; Pharmacyclics LLC, an AbbVie Company: Other: Travel Expenses, Research Funding, Speakers Bureau; Ohio State University: Patents & Royalties: OSU-2S; Ohio State University: Patents & Royalties: OSU-2S; Acerta: Research Funding; TG Therapeutics: Other: Travel Expenses, Research Funding, Speakers Bureau; Acerta: Research Funding; Pharmacyclics LLC, an AbbVie Company: Other: Travel Expenses, Research Funding, Speakers Bureau; Pharmacyclics LLC, an AbbVie Company: Other: Travel Expenses, Research Funding, Speakers Bureau; TG Therapeutics: Other: Travel Expenses, Research Funding, Speakers Bureau; Novartis: Other: Travel Expenses, Speakers Bureau; TG Therapeutics: Other: Travel Expenses, Research Funding, Speakers Bureau; Genentech: Research Funding; Genentech: Research Funding; Genentech: Research Funding; BeiGene: Research Funding; Novartis: Other: Travel Expenses, Speakers Bureau; Gilead: Other: Travel Expenses, Research Funding, Speakers Bureau; Gilead: Other: Travel Expenses, Research Funding, Speakers Bureau; BeiGene: Research Funding; Janssen: Consultancy, Other: Travel Expenses, Research Funding, Speakers Bureau; Acerta: Research Funding; BeiGene: Research Funding; Janssen: Consultancy, Other: Travel Expenses, Research Funding, Speakers Bureau; Novartis: Other: Travel Expenses, Speakers Bureau.