Background
Tumor characteristics affect surgical complexity and outcomes of partial nephrectomy (PN).
Objective
To develop an Arterial Based Complexity (ABC) scoring system to predict morbidity of PN.
Design, Setting, and Participants
Four readers independently scored contrast-enhanced computed tomography images of 179 patients who underwent PN.
Intervention
Renal cortical masses were categorized by the order of vessels needed to be transected/dissected during PN. Scores of 1, 2, 3S, or 3H were assigned to tumors requiring transection of interlobular and arcuate arteries, interlobar arteries, segmental arteries, or in close proximity of the renal hilum, respectively during PN.
Outcome Measurements and Statistical Analysis
Interobserver variability was assessed with kappa values and percentage of exact matches between each pairwise combination of readers. Linear regression was used to evaluate the association between reference scores and ischemia time, estimated blood loss, and estimated glomerular filtration rates (eGFR) at 6 wk and 6 mo after surgery adjusted for baseline eGFR. Fisher’s exact test was used to test for differences in risk of urinary fistula formation by reference category assignment.
Results and Limitations
Pairwise comparisons of readers’ score assignments were significantly correlated (all p <0.0001); average kappa = 0.545 across all reader pairs. The average proportion of exact matches was 69%. Linear regression between the complexity score system and surgical outcomes showed significant associations between reference category assignments and ischemia time (p <0.0001) and estimated blood loss (p = 0.049). Fisher’s exact test showed a significant difference in risk of urinary fistula formation with higher reference category assignments (p = 0.028). Limitations include use of a single institutional cohort to evaluate our system.
Conclusions
The ABC scoring system for PN is intuitive, easy to use, and demonstrated good correlation with perioperative morbidity.
Patient Summary
The ABC scoring system is novel anatomy-reproducible tool developed to help patients and doctors understand the complexity of renal masses and predict the outcomes of kidney surgery.