Preoperative risk calculator for the probability of completing nephron sparing for kidney cancer

In absence of predictive models, preoperative estimation of the probability of completing partial (PN) relative to radical nephrectomy (RN) is invariably inaccurate and subjective. We aimed to develop an evidence-based model to assess objectively the probability of PN completion based on patients' characteristics, tumor's complexity, urologist expertise and surgical approach.

675 patients treated with PN or RN for cT_1-2 cN₀ cM₀ renal mass by seven surgeons at one single experienced centre from 2000 to 2019.

The outcome of the study was PN completion. We used a multivariable logistic regression (MVA) model to investigate predictors of PN completion. We used SPARE score to assess tumor complexity. We used a bootstrap validation to compute the model's predictive accuracy. We investigated the relationship between the outcomes and specific predictors of interest such as tumor's complexity, approach and experience.

Of 675 patients, 360 (53%) were treated with PN vs. 315 (47%) with RN. Smaller tumors [Odds ratio (OR): 0.52, 95%CI 0.44-0.61; P < 0.001], lower SPARE score (OR: 0.67, 95%CI 0.47-0.94; P = 0.02), more experienced surgeons (OR: 1.01, 95%CI 1.00-1.02; P < 0.01), robotic (OR: 10; P < 0.001) and open (OR: 36; P < 0.001) compared to laparoscopic approach resulted associated with higher probability of PN completion. Predictive accuracy of the model was 0.94 (95% CI 0.93-0.95).

The probability of PN completion can be preoperatively assessed, with optimal accuracy relaying on routinely available clinical information. The proposed model might be useful in preoperative decision-making, patient consensus, or during preoperative counselling.

In patients with a renal mass the probability of completing a partial nephrectomy varies considerably and without a predictive model is invariably inaccurate and subjective. In this study we build-up a risk calculator based on easily available preoperative variables that can predict with optimal accuracy the probability of not removing the entire kidney.