Background: Currently two pretreatment prognostic models with limited accuracy (65-67%) can be used to predict survival in patients with localized renal cell carcinoma (RCC). Objective: We set out to develop a more accurate pretreatment model for predicting RCC-specific mortality after nephrectomy for all stages of RCC. Design, setting, and participants: The data originated from a series of prospectively recorded contemporary cases of patients treated with radical or partial nephrectomy between 1984 and 2006. Model development was performed using data from 2474 patients from five centers and external validation was performed using data from 1972 patients from seven centers. Measurements: The probability of RCC-specific mortality was modeled using Cox regression. The significance of the predictors was confirmed using competing risks analyses, which account for mortality from other causes. Results and limitations: Median follow-up in patients who did not die of RCC-specific causes was 4.2 yr and 3.5 yr in the development and validation cohorts, respectively. The freedom from cancer-specific mortality rates in the nomogram development cohort were 75.4% at 5 yr after nephrectomy and 68.3% at 10 yr after nephrectomy. All variables except gender achieved independent predictor status. In the external validation cohort the nomogram predictions were 88.1% accurate at 1 yr, 86.8% accurate at 2 yr, 86.8% accurate at 5 yr, and 84.2% accurate at 10 yr. Conclusions: Our model substantially exceeds the accuracy of the existing pretreatment models. Consequently, the proposed nomogram-based predictions may be used as benchmark data for pretreatment decision making in patients with various stages of RCC. (c) 2008 Published by Elsevier B.V. on behalf of European Association of Urology.
A Preoperative Prognostic Model for Patients Treated with Nephrectomy for Renal Cell Carcinoma
Suardi N;
2009-01-01
Abstract
Background: Currently two pretreatment prognostic models with limited accuracy (65-67%) can be used to predict survival in patients with localized renal cell carcinoma (RCC). Objective: We set out to develop a more accurate pretreatment model for predicting RCC-specific mortality after nephrectomy for all stages of RCC. Design, setting, and participants: The data originated from a series of prospectively recorded contemporary cases of patients treated with radical or partial nephrectomy between 1984 and 2006. Model development was performed using data from 2474 patients from five centers and external validation was performed using data from 1972 patients from seven centers. Measurements: The probability of RCC-specific mortality was modeled using Cox regression. The significance of the predictors was confirmed using competing risks analyses, which account for mortality from other causes. Results and limitations: Median follow-up in patients who did not die of RCC-specific causes was 4.2 yr and 3.5 yr in the development and validation cohorts, respectively. The freedom from cancer-specific mortality rates in the nomogram development cohort were 75.4% at 5 yr after nephrectomy and 68.3% at 10 yr after nephrectomy. All variables except gender achieved independent predictor status. In the external validation cohort the nomogram predictions were 88.1% accurate at 1 yr, 86.8% accurate at 2 yr, 86.8% accurate at 5 yr, and 84.2% accurate at 10 yr. Conclusions: Our model substantially exceeds the accuracy of the existing pretreatment models. Consequently, the proposed nomogram-based predictions may be used as benchmark data for pretreatment decision making in patients with various stages of RCC. (c) 2008 Published by Elsevier B.V. on behalf of European Association of Urology.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
