Singh AB, Bronsert MR, Henderson WG, et al. - Researchers sought the determine the utility of Surgical Risk Preoperative Assessment System (SURPAS), a parsimonious risk assessment tool using eight predictor variables developed from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) dataset, in accurately predicting discharge destination. SURPAS has been demonstrated as applicable to over 3,000 operations in adults in nine surgical specialties and predictive of important adverse outcomes. They developed a “full model” for risk of postoperative “discharge not to home” from 28 non-laboratory preoperative variables from ACS NSQIP 2012-17 dataset using logistic regression. A comparison of this with the eight-variable SURPAS model was done using the c-index as a measure of discrimination; the Hosmer-Lemeshow observed-to-expected plots testing calibration; and the Brier score, a combined metric of discrimination and calibration. They assessed 5,303,519 patients; of these, 447,153 (8.67%) experienced a discharge not to home. Outcomes support the efficacy of the eight-variable SURPAS model in predicting, preoperatively, the risk of postoperative discharge to a destination other than home as accurately as the 28 non-laboratory variable ACS NSQIP “full model.” Hence, they suggest incorporating discharge destination into the existing SURPAS tool, providing accurate outcomes to guide decision making and help prepare patients for their postoperative recovery.