Sina Khaneki1, Michael R Bronsert2, William G Henderson3, Maryam Yazdanfar1, Anne Lambert-Kerzner4, Karl E Hammermeister5, Robert A Meguid6. 1. Surgical Outcomes and Applied Research Program, University of Colorado School of Medicine, Aurora, CO, USA. 2. Surgical Outcomes and Applied Research Program, University of Colorado School of Medicine, Aurora, CO, USA; Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, CO, USA. 3. Surgical Outcomes and Applied Research Program, University of Colorado School of Medicine, Aurora, CO, USA; Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, CO, USA; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA. 4. Surgical Outcomes and Applied Research Program, University of Colorado School of Medicine, Aurora, CO, USA; Colorado School of Public Health, University of Colorado, Aurora, CO, USA. 5. Surgical Outcomes and Applied Research Program, University of Colorado School of Medicine, Aurora, CO, USA; Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, CO, USA; Division of Cardiology, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA. 6. Surgical Outcomes and Applied Research Program, University of Colorado School of Medicine, Aurora, CO, USA; Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, CO, USA; Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA. Electronic address: robert.meguid@ucdenver.edu.
Abstract
BACKGROUND: The novel Surgical Risk Preoperative Assessment System (SURPAS) requires entry of five predictor variables (the other three variables of the eight-variable model are automatically obtained from the electronic health record or a table look-up), provides patient risk estimates compared to national averages, is integrated into the electronic health record, and provides a graphical handout of risks for patients. The accuracy of the SURPAS tool was compared to that of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). METHODS: Predicted risk of postoperative mortality and morbidity was calculated using both SURPAS and ACS-SRC for 1,006 randomly selected 2007-2016 ACS National Surgical Quality Improvement Program (NSQIP) patients with known outcomes. C-indexes, Hosmer-Lemeshow graphs, and Brier scores were compared between SURPAS and ACS-SRC. RESULTS: ACS-SRC risk estimates for overall morbidity underestimated risk compared to observed postoperative overall morbidity, particularly for the highest risk patients. SURPAS accurately estimates morbidity risk compared to observed morbidity. CONCLUSIONS: SURPAS risk predictions were more accurate than ACS-SRC's for overall morbidity, particularly for high risk patients. SUMMARY: The accuracy of the SURPAS tool was compared to that of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). SURPAS risk predictions were more accurate than those of the ACS-SRC for overall morbidity, particularly for high risk patients.
BACKGROUND: The novel Surgical Risk Preoperative Assessment System (SURPAS) requires entry of five predictor variables (the other three variables of the eight-variable model are automatically obtained from the electronic health record or a table look-up), provides patient risk estimates compared to national averages, is integrated into the electronic health record, and provides a graphical handout of risks for patients. The accuracy of the SURPAS tool was compared to that of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). METHODS: Predicted risk of postoperative mortality and morbidity was calculated using both SURPAS and ACS-SRC for 1,006 randomly selected 2007-2016 ACS National Surgical Quality Improvement Program (NSQIP) patients with known outcomes. C-indexes, Hosmer-Lemeshow graphs, and Brier scores were compared between SURPAS and ACS-SRC. RESULTS: ACS-SRC risk estimates for overall morbidity underestimated risk compared to observed postoperative overall morbidity, particularly for the highest risk patients. SURPAS accurately estimates morbidity risk compared to observed morbidity. CONCLUSIONS: SURPAS risk predictions were more accurate than ACS-SRC's for overall morbidity, particularly for high risk patients. SUMMARY: The accuracy of the SURPAS tool was compared to that of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). SURPAS risk predictions were more accurate than those of the ACS-SRC for overall morbidity, particularly for high risk patients.
Authors: Neel P Chudgar; Shi Yan; Meier Hsu; Kay See Tan; Katherine D Gray; Daniela Molena; Tamar Nobel; Prasad S Adusumilli; Manjit Bains; Robert J Downey; James Huang; Bernard J Park; Gaetano Rocco; Valerie W Rusch; Smita Sihag; David R Jones; James M Isbell Journal: Ann Thorac Surg Date: 2020-10-16 Impact factor: 4.330
Authors: William G Henderson; Michael R Bronsert; Karl E Hammermeister; Anne Lambert-Kerzner; Robert A Meguid Journal: Patient Saf Surg Date: 2019-08-20
Authors: Nisha Pradhan; Adam R Dyas; Michael R Bronsert; Anne Lambert-Kerzner; William G Henderson; Howe Qiu; Kathryn L Colborn; Nicholas J Mason; Robert A Meguid Journal: Patient Saf Surg Date: 2022-03-17