To generate a nomogram based on preoperative parameters to predict the occurrence of a major complication within 30-days of robotic partial nephrectomy.
MATERIALS AND METHODS:
The study included 1,342 patients with a clinically localized renal tumor who underwent robotic partial nephrectomy (RPN) between 2010 and 2017 at 7 academic centers. The primary outcome was the major complication rate. A multivariable logistic regression model was fitted to predict the risk of major complications after RPN. Model-derived coefficients were used to calculate the risk of major complications. Local regression smoothing technique was used to plot the observed rate against the predicted risk of major complications.
In multivariate logistic regression, male gender (odds ratio [OR]: 2.93; P = 0.03), Charlson comorbidity index (OR: 1.13; P = 0.05), ECOG PS (OR: 1.66; P = 0.02), low hospital volume (P < 0.05), and high RENAL score (OR: 4.73; P = 0.01) were significant predictors of major postoperative complications. A preoperative nomogram incorporating these risk factors was constructed with an area under curve of 75%.
Using standard preoperative variables from this multi-institutional RPN experience, we constructed and validated a nomogram to predict postoperative complications after RPN. We believe this tool can be relevant to help weighing treatment options for a more tailored management of patients with small renal masses.