Nomogram predicting 30‐day mortality after nephrectomy in the contemporary era: Results from the SEER database

Abstract

Objectives To assess contemporary 30‐day mortality rates after partial and radical nephrectomy in USA, and to develop a predictive model of 30‐day mortality. Methods We relied on the National Cancer Institute Surveillance, Epidemiology and End Results database. A multivariable logistic regression analysis was fitted to predict 30‐day mortality. A nomogram was built based on the coefficients of the logit function. Internal validation was carried out using the leave‐one‐out cross‐validation. Calibration was graphically investigated. Results A total of 102 146 patients who underwent partial nephrectomy (n = 36 425; 35.7%) or radical nephrectomy (n = 65 721; 64.3%) between 2005 and 2015 were included in the analysis. The median age at diagnosis was 62 years. A total of 11 921 (11.7%) patients were African American. The clinical stage was T1–T2 in 79 452 (77.8%), T3 in 16 141 (15.8%) and T4/T1–4–M1 in 6553 (6.4%) patients. Overall, 497 deaths occurred during the initial 30 days after nephrectomy (0.49% 30‐day mortality rate). Stratified by type of surgery, the 30‐day mortality rate was 0.16% for partial nephrectomy and 0.67% for radical nephrectomy. At univariate analyses, age, tumor size, stage and surgical procedure emerged as predictors of 30‐day mortality (all P < 0.001). All of these covariates were included in the multivariable logistic regression model. The area under the curve after leave‐one‐out cross‐validation was 0.808 (95% confidence interval 0.788–0.828), and the model showed good calibration in the range of predicted probability <10%. Conclusions Contemporary rates of 30‐day mortality in patients undergoing radical or partial nephrectomy are very low. Age and tumor stage are key determinants of 30‐day mortality. We present a predictive model that provides individual probabilities of 30‐day mortality after nephrectomy, and it can be used for patient counseling prior surgery.

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