A Predictive Model for Kidney Failure After Nephrectomy for Localized Kidney Cancer: The Kidney Cancer Risk Equation

Abstract

Rationale & objective: Nephrectomy is the mainstay of treatment for individuals with localized kidney cancer. However, surgery can potentially result in the loss of kidney function or in kidney failure requiring dialysis/kidney transplantation. There are currently no clinical tools available to preoperatively identify which patients are at risk of kidney failure over the long term. Our study developed and validated a prediction equation for kidney failure after nephrectomy for localized kidney cancer.

Study design: Population-level cohort study.

Setting & participants: Adults (n=1,026) from Manitoba, Canada, with non-metastatic kidney cancer diagnosed between January 1, 2004, and December 31, 2016, who were treated with either a partial or radical nephrectomy and had at least 1 estimated glomerular filtration rate (eGFR) measurement before and after nephrectomy. A validation cohort included individuals in Ontario (n=12,043) with a diagnosis of localized kidney cancer between October 1, 2008, and September 30, 2018, who received a partial or radical nephrectomy and had at least 1 eGFR measurement before and after surgery.

New predictors & established predictors: Age, sex, eGFR, urinary albumin-creatinine ratio, history of diabetes mellitus, and nephrectomy type (partial/radical).

Outcome: The primary outcome was a composite of dialysis, transplantation, or an eGFR<15mL/min/1.73m² during the follow-up period.

Analytical approach: Cox proportional hazards regression models evaluated for accuracy using area under the receiver operating characteristic curve (AUC), Brier scores, calibration plots, and continuous net reclassification improvement. We also implemented decision curve analysis. Models developed in the Manitoba cohort were validated in the Ontario cohort.

Results: In the development cohort, 10.3% reached kidney failure after nephrectomy. The final model resulted in a 5-year area under the curve of 0.85 (95% CI, 0.78-0.92) in the development cohort and 0.86 (95% CI, 0.84-0.88) in the validation cohort.

Limitations: Further external validation needed in diverse cohorts.

Conclusions: Our externally validated model can be easily applied in clinical practice to inform preoperative discussions about kidney failure risk in patients facing surgical options for localized kidney cancer.

Plain-language summary: Patients with localized kidney cancer often experience a lot of worry about whether their kidney function will remain stable or will decline if they choose to undergo surgery for treatment. To help patients make an informed treatment decision, we developed a simple equation that incorporates 6 easily accessible pieces of patient information to predict the risk of reaching kidney failure 5 years after kidney cancer surgery. We expect that this tool has the potential to inform patient-centered discussions tailored around individualized risk, helping ensure that patients receive the most appropriate risk-based care.

Keywords: Kidney cancer; nephrectomy; onco-nephrology; prediction model; urology.