Introduction: The NHLBI supported Systolic Blood Pressure (SBP) Intervention Trial (SPRINT) (NCT01206062) aimed to identify an SBP target to reduce incidence of cardiovascular (CV) morbidity and mortality in hypertensive, non-diabetic patients of age ≥ 50 at increased CV risk. It found that intensive treatment (SBP target <120 mmHg) led to fewer major CV events and death but higher rates of adverse events. We reused publicly available patient-level SPRINT data from NHLBI Data Repository (BioLINCC) to perform hypothesis-generating secondary analyses by machine learning (ML) using random survival forest (RSF), to identify age specific baseline (bl) predictors for all-cause mortality (ACM).Methods: RSF was performed on 30 bl variables from 9361 patients in age group specific cohorts (50-59, 60-69, 70-79, 80-90). The identified top 10 predictors from each cohort were included in a multivariate analysis using a Cox proportional hazards model.Results: The top 10 predictors of ACM for age specific subgroups are shown in Figure 1. As expected, cardiovascular disease (CVD) predictors were selected, yet RSF distinctively identified renal biomarkers as important predictors, consistent with our previous analyses. Smoking status and history of CVD ranked as top predictors among age groups 50-59, 60-69, and 70-79. RSF also identified social factors, including race among age groups 60-69 and 80-90 and female gender among age groups 50-59 and 80-90 as important predictors for ACM. Lipid markers and medications used also showed up as top predictors. Specifically, polypharmacy emerged as a top predictor in age groups 60-69, 70-79, and 80-90, notably ranking higher in the 80-90 age group.Conclusions: Using ML, we uncovered in an unbiased fashion, unanticipated age specific top predictors for ACM in SPRINT trial. This highlights the value of ML for analyzing disease and therapeutic intervention outcomes and age specific prognostic factors to advance precision medicine.