A preoperative nomogram to predict major complications after robot assisted partial nephrectomy (UroCCR-57 study)

OBJECTIVE:

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.

RESULTS:

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%.

CONCLUSIONS:

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.