Abstract
BACKGROUND: Surgical risk prediction models traditionally use patient attributes and measures of physiology to generate predictions about postoperative outcomes. However, the surgeon's assessment of the patient may be a valuable predictor, given the surgeon's ability to detect and incorporate factors that existing models cannot capture. We compare the predictive utility of surgeon intuition and a risk calculator derived from the American College of Surgeons (ACS) NSQIP. STUDY DESIGN: From January 10, 2021 to January 9, 2022, surgeons were surveyed immediately before performing surgery to assess their perception of a patient's risk of developing any postoperative complication. Clinical data were abstracted from ACS NSQIP. Both sources of data were independently used to build models to predict the likelihood of a patient experiencing any 30-day postoperative complication as defined by ACS NSQIP. RESULTS: Preoperative surgeon assessment was obtained for 216 patients. NSQIP data were available for 9,182 patients who underwent general surgery (January 1, 2017 to January 9, 2022). A binomial regression model trained on clinical data alone had an area under the receiver operating characteristic curve (AUC) of 0.83 (95% CI 0.80 to 0.85) in predicting any complication. A model trained on only preoperative surgeon intuition had an AUC of 0.70 (95% CI 0.63 to 0.78). A model trained on surgeon intuition and a subset of clinical predictors had an AUC of 0.83 (95% CI 0.77 to 0.89). CONCLUSIONS: Preoperative surgeon intuition alone is an independent predictor of patient outcomes; however, a risk calculator derived from ACS NSQIP is a more robust predictor of postoperative complication. Combining intuition and clinical data did not strengthen prediction.