This article explores the combined use of immunoglobulin heavy chain variable (IGHV) gene mutation status and DNA sequence entropy analysis to assess survival outcomes in leukemia patients. Leveraging a chronic lymphocytic leukemia (CLL) patient database, we calculated various entropy metrics for individual DNA sequences. A randomized entropy divergence (EnRD) after as a consequence of sequence randomization, focusing on the disruption of neighborhood interrelation information, is employed to estimate patient-specific DNA statistical properties, enabling the categorization of patients into two distinct groups. Survival analyses integrate IGHV mutation subtypes with entropy measures, revealing statistically significant differences between groups with high and low randomized entropy divergence levels, as well as among IGHV subtypes. The findings suggest that patients with mutated IGHV genes, high DNA sequence entropy and high DNA sequence randomized entropy divergence exhibit notably improved survival prospects (Hazard Ratio HR = 5.23, p = 0.001). These results are validated through robust methodologies, including Kaplan-Meier survival curves, Cox regression analyses, and Linear Mixed Model (LMM). The study underscores the utility of combining IGHV mutational status with DNA entropy analysis as a prognostic tool, highlighting the potential to refine patient stratification and improve predictions of disease outcomes.