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Self-reported quality of life as a predictor of mortality in renal cell carcinoma
Ridwan Alam1, Hiten D Patel1, Zhuo T Su1
1James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Background:
This study evaluated the utility of self-reported quality of life (QOL) metrics in predicting mortality among all-comers with renal cell carcinoma (RCC) and externally tested the findings in a registry of patients with small renal masses.
Methods:
The Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey (SEER-MHOS) captured QOL metrics composed of mental component summary (MCS) and physical component summary (PCS) scores. Regression models assessed associations of MCS and PCS with all-cause, RCC-specific, and non-RCC-specific mortality. Harrell's concordance statistic (the C-index) and the Akaike information criterion (AIC) determined predictive accuracy and parsimony, respectively. Findings were tested in the prospective Delayed Intervention and Surveillance for Small Renal Masses (DISSRM) registry.
Results:
In SEER-MHOS, 1494 patients had a median age of 73.4 years and a median follow-up time of 5.6 years. Each additional MCS and PCS point reduced the hazard of all-cause mortality by 1.3% (95% CI, 0.981-0.993; P < .001) and 2.3% (95% CI, 0.971-0.984; P < .001), respectively. Models with QOL metrics demonstrated higher predictive accuracy (C-index, 72.3% vs 70.1%) and parsimony (AIC, 9376.5 vs 9454.5) than models without QOL metrics. QOL metrics exerted a greater effect on non-RCC-specific mortality than RCC-specific mortality. External testing in the DISSRM registry confirmed these findings with similar results for all-cause mortality.
Conclusions:
Models with self-reported QOL metrics predicted all-cause mortality in patients with RCC with higher accuracy and parsimony than those without QOL metrics. Physical health was a stronger predictor of mortality than mental health. The findings support the incorporation of QOL metrics into prognostic models and patient counseling for RCC.
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