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Accuracy of the Surgeons’ Clinical Prediction of Postoperative Major Complications Using a Visual Analog Scale

Background. Although the risk factors that contribute to postoperative complications are well recognized, prediction in the context of a particular patient is more difficult. We were interested in using a visual analog scale (VAS) to capture surgeons’ prediction of the risk of a major complication and to examine whether this could be improved. Methods. The study was performed in 3 stages. In phase I, the surgeon assessed the risk of a major complication on a 100-mm VAS immediately before and after surgery. A quality control questionnaire was designed to check if the VAS was being scored as a linear scale. In phase II, a VAS with 6 subscales for different areas of clinical risk was introduced. In phase III, predictions were completed following the presentation of detailed feedback on the accuracy of prediction of complications. Results. In total, 1295 predictions were made by 58 surgeons in 859 patients. Eight surgeons did not use a linear scale (6 logarithmic, 2 used 4 categories of risk). Surgeons made a meaningful prediction of major complications (preoperative median score 40 mm for complications v. 22 mm for no complication, P < 0.001; postoperative 46 mm v. 21 mm, P < 0.001). In phase I, the discrimination of prediction for preoperative (0.778), postoperative (0.810), and POSSUM (Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity) morbidity (0.750) prediction was similar. Although there was no improvement in prediction with a multidimensional VAS, there was a significant improvement in the discrimination of prediction after feedback (preoperative, 0.895; postoperative, 0.918). Conclusion. Awareness of different ways a VAS is scored is important when designing and interpreting studies. Clinical assessment of major complications by the surgeon was initially comparable to the prediction of the POSSUM morbidity score and improved significantly following the presentation of clinically relevant feedback.