Association between Health-Related Quality of Life measures with bladder cancer characteristics and survival outcomes in older bladder cancer patients : a clustering approach
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[EMBARGOED UNTIL 12/01/2024] Introduction: Bladder cancer significantly impacts the physical and mental well-being of elderly patients. This study employs clustering algorithms to 1) categorize patient survival based on Health-Related Quality of Life (HRQOL) and Activities of Daily Living (ADLs), 2) explore the influence of bladder cancer characteristics on HRQOL, and 3) evaluate changes in patient clusters over a two-year follow-up. Methods: Data from bladder cancer patients aged 65 and above in the SEER-MHOS database (1998-2020) were analyzed using machine learning algorithms. Clusters of elderly bladder cancer patients with similar HRQOL (measured by PCS, MCS, and ADLs) were identified, and the log-rank test was employed to assess their association with survival outcomes. Results: The analysis revealed three distinct clusters, showing significant differences in demographics and socioeconomic variables (excluding smoking status, p=0.26). Variations in outcomes, such as depression and falls, were observed. Kaplan-Meier survival analysis with the log-rank test indicated substantial differences in survival probabilities among clusters. However, no significant differences were found in bladder cancer characteristics, including cancer staging (p=0.12), surgical type (p=0.07), and time from the first cancer diagnosis to the survey (p=0.50) among the clusters. Conclusion: The study suggests the independence of HRQOL measures (PCS, MCS, and ADLs) from bladder cancer characteristics. These findings provide valuable insights for urologists to guide patients on the potential impact of their functional status on cancer outcomes and overall well-being, enhancing patient care and understanding the link between cancer, its treatments, and emotional and physical health.
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M.S.