695 ACS NSQIP – personalised risk prediction tool for postoperative complications in gynaeoncology surgery?

Introduction/Background* Despite the informed consent process, patients’ understanding of potential post-operative complications is often limited, making it difficult to call the decision an informed one, so estimating the risk of postoperative complications is important for shared decision making and to help multidisciplinary teams plan postoperative care. Increased incidence of gynaecological cancers and operations, especially technically challenging minimally invasive surgery (MIS) in older, obese and patients with multiple comorbidities, requires accurate prediction of the likelihood of mortality and morbidity and patient involvement in joint decision making about the management. ACS-NSQIP (American College of Surgeons National Surgical Quality Improvement Program) risk calculator is a validated web-based tool based on 21 preoperative risk factors to predict 8 post-operative outcomes. The objective of our study was to explore the validity of ACS NSQIP in gynaeoncology for perioperative prediction of postoperative complications. Methodology A retrospective multicentre cohort study evaluated 1552 patients who underwent surgery at a tertiary oncology centre. Data collection undertaken through dedicated database. Data collated on 764 patients undergoing robotic, 248 laparoscopic and 540 open surgery for suspected or confirmed gynaecological malignancy. All missing data collated from patient notes. Following data lock with the actual post-op event/complication that occurred in this retrospective cohort, ACS NSQIP used to count predictive scores for each patient. Data analysis evaluating ACS-NSQIP validity and relevance in gynaecological oncology patients and its ability to predict postoperative complications performed. Result(s)* ACS-NSQIP was found to best predict mortality (AUC - 0.908), it was most accurate for prediction of complications as follows: discharge to rehabilitation (AUC-0.875), cardiac complications (AUC-0.854), sepsis (AUC-0.795), pneumonia (AUC-0.764), return to theatre (AUC-0.696), surgical site infection (AUC-0.686), VTE (AUC-0.676), readmission (AUC-0.669), renal failure (AUC-0.601). Poor result in the prediction of UTI (AUC-0.543) was noted. Conclusion* ACS-NSQIP risk calculator appears to predict major complications and post-operative mortality making it useful as an informed consent tool. Preliminary data suggests that further validation is required to fully evaluate if the risk scores may be used to inform patients pre-operatively of their risk of complications and is currently being undertaken.