Introduction: Advanced data analytics are needed to reliably predict bleeding and thrombotic risk in ambulatory left ventricular assist device (LVAD) patients. Hypothesis: Machine learning techniques can be used to predict risk of gastrointestinal bleeding (GIB), stroke, or death in ambulatory LVAD patients. Methods: HeartMate 3 TM LVAD recipients from the MOMENTUM 3 studies with up to 2-year follow-up were included. A multistate model was developed with 5-fold cross-validation to characterize the continuous probability of events after discharge. Model features included pre-implant, index implant, and short- and long-term post-implant clinical features. 85% of patients were used for model derivation and 15% for validation. Model performance was assessed with area under the curve (AUC). With the model features, a risk stratification tool was created by dividing patients’ into terciles of predicted risk. Results: Among 2,056 LVAD patients who survived to hospital discharge, the median age was 59.4 yrs (20.4% Female, 28.6% Black). At 2-years, the incidence of GIB, Stroke, and Death was 25.6%, 6.0% and 12.3%, respectively. Unique models including pre-implant, implant and post-implant features were created for GIB (Figure A), Stroke and Death. In ambulatory LVAD patients, the model could predict 30-day risk of GIB at any time post-implant which was 26.9%, 2.6% and 0.8% in high, medium and low-risk patients respectively (Figure B). Similar risk prediction was performed for stroke and death. The cross-validated AUC in the derivation cohort was 0.70, 0.69, and 0.85 for GIB, stroke, and death respectively. Conclusion: We developed an innovative risk tool informed by machine learning techniques that predicts risk of GIB, stroke, and death at any 30-day interval in ambulatory LVAD patients with readily available clinical data. The model allows for risk stratification that accurately predicts future events and may be useful to guide clinical decision making.