Robotic oncologic complexity score - a new tool for predicting complications in computer-enhanced oncologic surgery

Abstract

BACKGROUND: While there is little doubt that robotic interventions have already opened new horizons in surgery due to its inherent complexity, there is still an unmet need for tools allowing center-to-center performance comparisons. A complexity score could be a valuable instrument for further research. METHODS: The items of the robotic oncologic complexity score (ROCS) were based on risk factors identified in previous studies. We attempt to build the score and validate it on 400 consecutive cases of robotic oncologic surgery. The primary endpoint is to assess the value of ROCS in predicting major complications. RESULTS: The mean ROCS in the group was 3.3(+/-1.4). Different correlations were calculated: the score and the complications (r=0.38), the major complications (r=0.42), Clavien grade (r=0.5), the operating time (r=0.35), and the length of stay (r=0.47). On the ROC-curve a score >4 has the best specificity and sensibility for predicting major complications (P<0.05). CONCLUSION: ROCS has potential in predicting complications and hospital length of stay, as well as a role in classifying oncologic robotic surgical interventions. Copyright © 2015 John Wiley & Sons, Ltd.