A preoperative prognostic model for patients treated with nephrectomy for renal cell carcinoma.
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.


METHODS
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.


METHODS
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.


CONCLUSIONS
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.
Karakiewicz
Pierre I
PI
Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, QC, Canada. pierre.karakiewicz@umontreal.ca
Suardi
Nazareno
N
Capitanio
Umberto
U
Jeldres
Claudio
C
Ficarra
Vincenzo
V
Cindolo
Luca
L
de la Taille
Alexandre
A
Tostain
Jacques
J
Mulders
Peter F A
PF
Bensalah
Karim
K
Artibani
Walter
W
Salomon
Laurent
L
Zigeuner
Richard
R
Valéri
Antoine
A
Descotes
Jean-Luc
JL
Rambeaud
Jean-Jacques
JJ
Méjean
Arnaud
A
Montorsi
Francesco
F
Bertini
Roberto
R
Patard
Jean-Jacques
JJ
D000704:Analysis of Variance; D002292:Carcinoma, Renal Cell / Q000000981:diagnostic imaging / Q000401:mortality / Q000473:pathology / Q000601:surgery*; D002423:Cause of Death; D015331:Cohort Studies; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D008297:Male; D015999:Multivariate Analysis; D009367:Neoplasm Staging; D009392:Nephrectomy / Q000379:methods* / Q000401:mortality; D011336:Probability; D011379:Prognosis; D011446:Prospective Studies; D012044:Regression Analysis; D015203:Reproducibility of Results; D015996:Survival Rate; D017741:Survivors; D013997:Time Factors; D014057:Tomography, X-Ray Computed