Predictive models of suicide risk have focused on predictors extracted from structured data found in electronic health records (EHR), with limited consideration of negative life events (LE) expressed in unstructured clinical text such as housing instability, marital troubles, etc. Additionally, there has been limited work in large-scale analysis of natural language processing (NLP) derived predictors for suicide risk and integration of extracted LE into longitudinal and predictive models of suicide risk. Our study aims to expand upon previous research, showing how large language models (LLM) and high-performance computing (HPC) can be used to annotate LE spanning over 22 years in the Veterans Affairs (VA) corporate data warehouse (CDW) with enriched sensitivity and demonstrate trends for acute suicide risk. Many Veteran timelines reference more than one LE in unstructured clinical text by the time a suicide-related diagnosis was recorded. Longitudinal data from extractions serve as acute predictors of suicide-related events. Preliminary analysis of ascertain administrative bias in NLP extractions show many mentions occur prior to triaging by case-coordinators. Lastly, LE provide essential input that improves the performance of predictive modeling concerning suicide-related events.