Evaluating overall survival and competing risks of death in patients with localized renal cell carcinoma using a comprehensive nomogram

Abstract

Purpose: Many patients with localized node-negative renal cell carcinoma (RCC) are elderly with competing comorbidities. Their overall survival benefit after surgical treatment is unknown. We reviewed cases in the Surveillance, Epidemiology, and End Results (SEER) database to evaluate the impact of kidney cancer versus competing causes of death in patients with localized RCC and develop a comprehensive nomogram to quantitate survival differences.

Methods: We identified individuals with localized, surgically treated clear-cell, papillary, or chromophobe RCC in SEER (1988 through 2003). We used Fine and Gray competing risks proportional hazards regressions to predict 5-year probabilities of three competing mortality outcomes: kidney cancer death, other cancer death, and noncancer death.

Results: We identified 30,801 cases of localized RCC (median age, 62 years; median tumor size, 4.5 cm). Five-year probabilities of kidney cancer death, other cancer death, and noncancer death were 4%, 7%, and 11%, respectively. Age was strongly predictive of mortality and most predictive of nonkidney cancer deaths (P < .001). Increasing tumor size was related to death from RCC and inversely related to noncancer deaths (P < .001). Racial differences in outcomes were most pronounced for nonkidney cancer deaths (P < .001). Men were more likely to die than women from all causes (P < .002). This nomogram integrates commonly available factors into a useful tool for comparing competing risks of death.

Conclusion: Management of localized RCC must consider competing causes of mortality, particularly in elderly populations. Effective decision making requires treatment trade-off calculations. We present a tool to quantitate competing causes of mortality in patients with localized RCC.

Fig 1.

Marginal cumulative incidence curves with 95% CIs for the three types of death included in the predictive model.

Fig 2.

Predicted probability of (A) overall survival by age shown using Kaplan- Meier curve, (B) kidney cancer–specific survival (determined using codes 29010, 29020, 29030, and 29040) by tumor size shown using (1−) cumulative incidence function, and (C) non–kidney cancer specific survival by race shown using (1−) cumulative incidence function.

Fig 3.

Calibration after grouping individuals by decile of regression predicted 5-year probabilities.

Fig 4.

Nomogram evaluating 5-year competing risks of death in patients with localized renal cell carcinoma. Total point values are independently calculated for each cause of death and then applied to the corresponding probability scale at the bottom of the figure. For example, a 75-year old white male with a 4-cm tumor would have a 5-year mortality of 5% (80 points) from RCC versus 4.5% (114 points) from other cancers and 14% (91 points) from noncancerous causes.