Evaluation of the performance of the ACS NSQIP surgical risk calculator in gyn oncology patients undergoing laparotomy

abstract

Highlights

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The NSQIP calculator adequately predicted death, cardiovascular and renal complications.

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The NSQIP calculator did not accurately predict common complications.

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The NSQIP calculator tended to under-estimate hospital stay by one day.

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Objective

The objective of this study was to evaluate the ability of the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) surgical risk calculator to predict complications in gynecologic oncology patients undergoing laparotomy.

Methods

A chart review of patients who underwent laparotomy on the gynecologic oncology service at a single academic hospital from January 2009 to December 2013 was performed. Preoperative variables were abstracted and NSQIP surgical risk scores were calculated. The risk of any complication, serious complication, death, urinary tract infection, venous thromboembolism, cardiac event, renal complication, pneumonia and surgical site infection were correlated with actual patient outcomes using logistic regression. The c-statistic and Brier score were used to calculate the prediction capability of the risk calculator.

Results

Of the 1094 patients reviewed, the majority were < 65 years old (70.9%), independent (95.2%), ASA class 1–2 (67.3%), and overweight or obese (76.1%). Higher calculated risk scores were associated with an increased risk of the actual complication occurring for all events (p < 0.05). The calculator performed best for predicting death (c-statistic = 0.851, Brier = 0.008), renal failure (c-statistic = 0.752, Brier = 0.015) and cardiac complications (c-statistic = 0.708, Brier = 0.011). The calculator did not accurately predict most complications.

Conclusions

The NSQIP surgical risk calculator adequately predicts specific serious complications, such as postoperative death and cardiac complications. However, the overall performance of the calculator was worse for gynecologic oncology patients than reported in general surgery patients. A tailored prediction model may be needed for this patient population.