MP36-15 SELF-REPORTED QUALITY OF LIFE FOR PREDICTING MORTALITY IN RENAL CELL CARCINOMA

Abstract

You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging II1 Apr 2018MP36-15 SELF-REPORTED QUALITY OF LIFE FOR PREDICTING MORTALITY IN RENAL CELL CARCINOMA Ridwan Alam, Hiten D. Patel, Michael A. Gorin, Michael H. Johnson, Mohamad E. Allaf, and Phillip M. Pierorazio Ridwan AlamRidwan Alam More articles by this author , Hiten D. PatelHiten D. Patel More articles by this author , Michael A. GorinMichael A. Gorin More articles by this author , Michael H. JohnsonMichael H. Johnson More articles by this author , Mohamad E. AllafMohamad E. Allaf More articles by this author , and Phillip M. PierorazioPhillip M. Pierorazio More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.1149AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Quality of life (QOL) in cancer patients has gained increasing attention and may provide prognostic value above and beyond traditional demographic and disease parameters. We evaluate the utility of self-reported QOL to predict mortality in patients with renal cell carcinoma (RCC). METHODS The Medicare Health Outcomes Survey was linked to the SEER database to identify patients who completed a QOL questionnaire after the diagnosis of RCC from 1998-2014. Mental component summary (MCS) and physical component summary (PCS) scores were classified as high (≥50) or low (<50) based on a population mean score of 50 points. Patients were classified into four groups: 1) high MCS, high PCS; 2) high MCS, low PCS; 3) low MCS, high PCS; and 4) low MCS, low PCS. Multivariable Cox proportional hazards regression evaluated associations between QOL and all-cause mortality (ACM). The Harrell's concordance statistic (C-index) estimated the model's predictive accuracy. Fine and Gray competing risks models adjusting for disease extent, demographics, and comorbidities evaluated the incidence of RCC-specific and non-RCC-specific mortality. RESULTS A total of 1494 patients with a median age of 73.4 years (IQR 68.8-79.3) at survey completion were included. Median follow-up was 5.6 years (IQR 4.0-8.3). There were 747 deaths, of which 139 were due to RCC. Cox regression showed that each additional MCS and PCS point reduced the hazard of ACM by 1.3% (95% CI 0.981-0.993, P<0.001) and 2.2% (95% CI 0.972-0.985, P<0.001), respectively. The C-index was 72.1%. In the competing risks model, the subdistribution hazard ratio (SHR) of RCC mortality in Groups 2, 3, and 4 were 2.71 (95% CI 1.18-6.22, P=0.02), 4.55 (95% CI 1.57-13.18, P=0.005), and 3.11 (95% CI 1.35-7.16, P=0.008), respectively, compared to Group 1 [Figure A]. The SHR for non-RCC mortality in Groups 2, 3, and 4 were 1.50 (95% CI 1.16-1.94, P=0.002), 1.03 (95% CI 0.59-1.78, P=0.9), and 1.83 (95% CI 1.41-2.38, P<0.001), respectively, relative to Group 1 [Figure B]. CONCLUSIONS Self-reported QOL metrics can be used to predict ACM in RCC patients with good accuracy. Lower PCS and MCS scores led to higher rates of ACM, even after accounting for differences in disease, demographics, and comorbidity. Furthermore, non-RCC mortality was associated more with low physical health rather than low mental health. © 2018FiguresReferencesRelatedDetailsCited bySotimehin A, Patel H, Alam R, Gorin M, Johnson M, Chang P, Wagner A, McKiernan J, Allaf M and Pierorazio P (2019) Selecting Patients with Small Renal Masses for Active Surveillance: A Domain Based Score from a Prospective Cohort StudyJournal of Urology, VOL. 201, NO. 5, (886-892), Online publication date: 1-May-2019. Volume 199Issue 4SApril 2018Page: e463-e464 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Ridwan Alam More articles by this author Hiten D. Patel More articles by this author Michael A. Gorin More articles by this author Michael H. Johnson More articles by this author Mohamad E. Allaf More articles by this author Phillip M. Pierorazio More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...