Development of a simple nomogram to estimate risk for intraoperative complications before partial nephrectomy based on the Mayo Adhesive Probability score combined with the RENAL nephrometry score

Predictive Value of the Mayo Adhesive Probability (MAP) Score in Laparoscopic Partial Nephrectomies: A Systematic Review from the EAU Section of Uro-Technology (ESUT)

The Mayo Adhesive Probability (MAP) score is a radiographic scoring system that predicts the presence of adherent perinephric fat (APF) during partial nephrectomies (PNs). The purpose of this systematic review is to summarize the current literature on the application of the MAP score for predicting intraoperative difficulties related to APF and complications in laparoscopic PNs. Three databases, PubMed, Scopus and Cochrane, were screened, from inception to 29 October 2023, taking into consideration the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines. All the inclusion criteria were met by eight studies. The total operative time was around two hours in most studies, while the warm ischemia time was <30 min in all studies and <20 min in four studies. Positive surgical margins, conversion and transfusion rates ranged from 0% to 6.3%, from 0% to 5.0% and from 0.7% to 7.5%, respectively. Finally, the majority of the complications were classified as Grade I-II, according to the Clavien-Dindo Classification System. The MAP score is a useful tool for predicting not only the presence of APF during laparoscopic PNs but also various intraoperative and postoperative characteristics. It was found to be significantly associated with an increased operative time, estimated blood loss and intraoperative and postoperative complication rates.

Development and Validation of a Nomogram Predicting Intraoperative Adverse Events During Robot-assisted Partial Nephrectomy

BACKGROUND

Ability to predict the risk of intraoperative adverse events (IOAEs) for patients undergoing partial nephrectomy (PN) can be of great clinical significance.

OBJECTIVE

To develop and internally validate a preoperative nomogram predicting IOAEs for robot-assisted PN (RAPN).

DESIGN, SETTING, AND PARTICIPANTS

In this observational study, data for demographic, preoperative, and postoperative variables for patients who underwent RAPN were extracted from the Vattikuti Collective Quality Initiative (VCQI) database.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

IOAEs were defined as the occurrence of intraoperative surgical complications, blood transfusion, or conversion to open surgery/radical nephrectomy. Backward stepwise logistic regression analysis was used to identify predictors of IOAEs. The nomogram was validated using bootstrapping, the area under the receiver operating characteristic curve (AUC), and the goodness of fit. Decision curve analysis (DCA) was used to determine the clinical utility of the model.

RESULTS AND LIMITATIONS

Among the 2114 patients in the study cohort, IOAEs were noted in 158 (7.5%). Multivariable analysis identified five variables as independent predictors of IOAEs: RENAL nephrometry score (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.02-1.25); clinical tumor size (OR 1.01, 95% CI 1.001-1.024); PN indication as absolute versus elective (OR 3.9, 95% CI 2.6-5.7) and relative versus elective (OR 4.2, 95% CI 2.2-8); Charlson comorbidity index (OR 1.17, 95% CI 1.05-1.30); and multifocal tumors (OR 8.8, 95% CI 5.4-14.1). A nomogram was developed using these five variables. The model was internally valid on bootstrapping and goodness of fit. The AUC estimated was 0.76 (95% CI 0.72-0.80). DCA revealed that the model was clinically useful at threshold probabilities >5%. Limitations include the lack of external validation and selection bias.

CONCLUSIONS

We developed and internally validated a nomogram predicting IOAEs during RAPN.

PATIENT SUMMARY

We developed a preoperative model than can predict complications that might occur during robotic surgery for partial removal of a kidney. Tests showed that our model is fairly accurate and it could be useful in identifying patients with kidney cancer for whom this type of surgery is suitable.