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Nomogram for predicting the likelihood of postoperative surgical complications in patients treated with partial
Andrea Mari1, Riccardo Campi1, Riccardo Schiavina2,3
1Unit of Oncologic Minimally-Invasive Urology and Andrology, Department of Urology, Careggi Hospital, University of Florence, Florence, Italy.
Objective:
To identify meaningful predictors and to develop a nomogram of postoperative surgical complications in patients treated with partial nephrectomy (PN).
Patients And Methods:
We prospectively evaluated 4308 consecutive patients who had surgical treatment for renal tumours, between 2013 and 2016, at 26 Italian urological centres (RECORd 2 project). A multivariable logistic regression for surgical complications was performed. A nomogram was created from the multivariable model. Internal validation processes were performed using bootstrapping with 1000 repetitions.
Results:
Overall, 2584 patients who underwent PN were evaluated for the final analyses. The median (interquartile [IQR]) American Society of Anesthesiologists (ASA) score was 2 (2-3). In all, 72.4% of patients had clinical T1a (cT1a) stage tumours. The median (IQR) Preoperative Aspects and Dimensions Used for an Anatomical (PADUA) score was 7 (6-8). Overall, 34.3%, 27.7%, 38% of patients underwent open PN (OPN), laparoscopic PN (LPN), and robot-assisted PN (RAPN). Overall and major postoperative surgical complications were recorded in 10.2% and 2.5% of patients, respectively. At multivariable analysis, age, ASA score, cT2 vs cT1a stage, PADUA score, preoperative anaemia, OPN and LPN vs RAPN, were significant predictive factors of postoperative surgical complications. We used these variables to construct a nomogram for predicting the risk of postoperative surgical complications. At decision curve analysis, the nomogram led to superior outcomes for any decision associated with a threshold probability of >5%.
Conclusion:
Several clinical predictors have been associated with postoperative surgical complications after PN. We used this information to develop and internally validate a nomogram to predict such risk.
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