LAUSR

BACKGROUND With inpatient length of stay decreasing, discharge destination following surgery can serve as an important metric for quality of care. Additionally, patients desire information on possible discharge destination. Adequate planning requires a multidisciplinary approach, can reduce health care costs and ensure patient needs are met. The Surgical Risk Preoperative Assessment System (SURPAS) is a parsimonious risk assessment tool using eight predictor variables developed from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) dataset. SURPAS is applicable to over 3,000 operations in adults in nine surgical specialties, predicts important adverse outcomes, and is incorporated into our electronic health record. We sought to determine if SURPAS can accurately predict discharge destination. STUDY DESIGN A "full model" for risk of postoperative "discharge not to home" was developed from 28 non-laboratory preoperative variables from ACS NSQIP 2012-17 dataset using logistic regression. This was compared to the eight-variable SURPAS model 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. RESULTS Of 5,303,519 patients, 447,153 (8.67%) experienced a discharge not to home. The SURPAS model's c-index, 0.914, was 99.24% of that of the "full model's", 0.921; the Hosmer-Lemeshow plots indicated good calibration; and the Brier score was 0.0537 and 0.0514 for the SUPAS and full model respectively. CONCLUSION The eight-variable SURPAS model preoperatively predicts risk of postoperative discharge to a destination other than home as accurately as the 28 non-laboratory variable ACS NSQIP "full model." Therefore, discharge destination can be integrated into the existing SURPAS tool, providing accurate outcomes to guide decision making and help prepare patients for their postoperative recovery.