Chronic kidney disease (CKD) significantly increases the risk of CVD diseases, particularly among elderly patients. Understanding the interaction between several biomarkers and cardiovascular (CVD) risks is crucial for improving patient outcomes and tailoring personalized treatment strategies. There is much more to learn about the intricate relationship between biomarkers and CVD risks in elderly CKD patients. Research aims to harness natural language processing (NLP) strategies to investigate the interaction between biomarkers and CVD risks in elderly patients with CKD. This research examined how changes in baseline values of four novel and classic cardiac biomarkers relate to the danger of CVD, and all-cause death in a large cohort of patients with CKD. Initially, medical data were collected from EHR of elderly CKD patients. NLP technique, such as Named Entity Recognition (NER), is used to extract the relevant biomarkers and CVD risk factors from the data. Statistical techniques were applied to examine the associations between biomarkers and CVD risks. The predictive models, using a combination of structured and NLP-extracted features demonstrated improved accuracy in forecasting CVD outcomes compared to traditional methods. This investigation highlights the critical role of specific biomarkers like PTH and FGF-23 in predicting CVD outcomes, providing insights into the possibility of using EHR data for better patient management and enhancing predictive models for this high-risk population.