Sharma G, Shah M, Ahluwalia P, Dasgupta P, Challacombe BJ, Bhandari M, Ahlawat R, Rawal S, Buffi NM, Sivaraman A, Porter JR, Rogers C, Mottrie A, Abaza R, Rha KH, Moon D, Yuvaraja TB, Parekh DJ, Capitanio U, Maes KK, Porpiglia F, Turkeri L, and Gautam G. Development and Validation of a Nomogram Predicting Intraoperative Adverse Events During Robot-assisted Partial Nephrectomy. Eur Urol Focus 2022.
Eur Urol Focus
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.
ePub ahead of print