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To refine outcome predictions, we investigated the use of microbial\u2010derived biomarkers to predict composite complete remission (CRc), relapse, and survival for patients on high\u2010 and low\u2010intensity regimens, and to integrate those variables into the widely clinically utilized European Leukemia Network (ELN\u20102022) genetic risk classification model for high\u2010intensity\u2010treated patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We first developed machine learning models that integrate baseline fecal metabolomics, 16S rRNA\u2010based stool microbiome features, and clinical metadata (sex, antibiotic administration, AML somatic mutations, and cytogenetics) from two cohorts of AML patients (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200983) undergoing remission induction chemotherapy. Univariate tests and sparse canonical correlation analysis were employed for variable selection and to explore fecal metabolite\u2010microbe relationships. A robust machine learning approach using XGBoost was employed, with 100 stratified data splits (80% training, 20% testing) and coarse\u2010to\u2010fine hyperparameter optimization. Variable importance was aggregated across all models to select key predictors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For high\u2010intensity\u2010treated patients, XGBoost models achieved aggregated AUROC scores of 0.719, 0.729, and 0.65 for CRc, relapse, and overall survival, respectively. For low\u2010intensity\u2010treated patients, these models achieved aggregate AUROC scores of 0.945, 0.724, and 0.768 for these same outcomes, respectively. Integrating the biomarkers identified in the high\u2010intensity machine\u2010learning models with the current ELN\u20102022 AML risk stratification system effectively stratified patients into risk categories, which obtained higher concordance indices and likelihood ratios, demonstrating improved prognostic accuracy for each outcome compared to ELN\u20102022 alone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The inclusion of microbial\u2010derived biomarkers serves as a robust prognostic tool to improve outcome prediction in AML patients, highlighting the potential of its integration into AML risk assessment and paving the way for personalized treatment strategies and improved patient outcomes.<\/jats:p>\n                  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