Approximately 15% of adults in the United States (U.S.) are aicted with chronic kidney disease (CKD). For CKD patients, the progressive decline of kidney function is intricately related to hospitalizations due to cardiovascular disease (CVD) and eventual \terminal events, such as kidney failure and mortality. To unravel the mechanisms underlying the disease dynamics of these interdependent processes, including identifying inuential risk factors, as well as tailoring decision-making to individual patient needs, we develop a novel Bayesian multivariate joint model (BM-JM) for the intercorrelated outcomes of kidney function (as measured by longitudinal estimated glomerular ltration rate), recurrent cardiovascular events, and competing-risk terminal events of kidney failure and death. The proposed joint modeling approach not only facilitates the exploration of risk factors associated with each outcome, but also allows dynamic updates of cumulative incidence probabilities for each competing risk for future subjects based on their basic characteristics and a combined history of longitudinal measurements and recurrent events. We propose ecient and exible estimation and prediction procedures within a Bayesian framework employing Markov Chain Monte Carlo (MCMC) methods. The predictive performance of our model is assessed through dynamic area under the receiver operating characteristic (ROC) curves (AUC) and the expected Brier score (BS).We demonstrate the ecacy of the proposed methodology through extensive simulations. Proposed methodology is applied to data from the Chronic Renal Insuciency Cohort (CRIC) study established by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) to address the rising epidemic of CKD in the U.S.