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

Purpose: 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. Design, setting and participants: 675 patients treated with PN or RN for cT1-2 cN0 cM0 renal mass by seven surgeons at one single experienced centre from 2000 to 2019. Outcomes measurements and statistical analyses: 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. Results: 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). Conclusions: 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. Patient summary: 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.