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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Adolescent, Adult, Aged, Aged, 80 and over, Analysis of Variance, Carcinoma, Renal Cell, Child, Disease-Free Survival, Female, Humans, Kidney Neoplasms, Male, Middle Aged, Nephrectomy, Prognosis, Severity of Illness Index.
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Do eCOG performance status 0 or 1 and symptom classification improve the ability to predict renal cell carcinoma-specific survival? Context: We tested and compared the improvement in prognostic ability related to the consideration of either ECOG performance status (ECOGPS) and/or symptom classification (S-CLASS) in renal cell carcinoma specific mortality (RCC-SM) predictions. Univariate and multivariate Cox regression analyses targeted RCC-SM in 2570 RCC patients treated with either partial or radical nephrectomy. The increment in predictive accuracy related to the addition of either ECOGPS, S-CLASS or both was quantified using Harrell's concordance index. Follow-up ranged from 0.1 to 23 years (median 3.2) and 610 patients (23.7%) died of RCC. In multivariable analyses, ECOGPS and S-CLASS represented independent predictors of RCC-SM. The addition of ECOGPS to established RCC-SM predictors increased the predictive accuracy by 0.3% (p=0.8) versus 0.6% (p=0.5) for S-CLASS versus 0.6% (p=0.5) for both.
assistant
Neither ECOGPS nor S-CLASS improves the ability to predict RCC-SM. Therefore, these variables may be safely omitted when RCC-SM risk is quantified. Final Decision: no