Dimitrios E Magouliotis1,2, David Walker1, Ioannis Baloyiannis2, Maria P Fergadi3, Ioannis Mamaloudis2, Georgios Chasiotis2, George A Tzovaras4. 1. Division of Surgery and Interventional Science, Faculty of Medical Sciences, UCL, London, UK. 2. Department of Surgery, University of Thessaly, Biopolis, 41110, Larissa, Greece. 3. Department of Radiology, University of Thessaly, Biopolis, Larissa, Greece. 4. Department of Surgery, University of Thessaly, Biopolis, 41110, Larissa, Greece. gtzovaras@med.uth.gr.
Abstract
BACKGROUND: The accurate evaluation of perioperative risk is crucial to facilitate the shared decision-making process. Surgical outcome risk tool (SORT) has been developed to provide enhanced and more feasible identification of high-risk surgical patients. Nonetheless, SORT has not been validated for patients with colorectal cancer undergoing surgery. Our aim was to determine whether SORT can accurately predict mortality after surgery for colorectal cancer and to compare it with traditional risk models. METHOD: 526 patients undergoing surgery performed by a colorectal surgical team in a single Greek tertiary hospital (2011-2019) were included. Five risk models were evaluated: (1) SORT, (2) Physiology and Operative Severity Score for the enumeration of Mortality and Morbidity (POSSUM), (3) Portsmouth POSSUM (P-POSSUM), (4) Colorectal POSSUM (CR-POSSUM), and (5) the Association of Great Britain and Ireland (ACPGBI) score. Model accuracy was assessed by observed to expected (O:E) ratios, and area under Receiver Operating Characteristic curve (AUC). RESULTS: Ten patients (1.9%) died within 30 days of surgery. SORT was associated with an excellent level of discrimination [AUC:0.81 (95% CI:0.68-0.94); p = 0.001] and provided the best performing calibration of all models in the entire dataset analysis (H-L:2.82; p = 0.83). Nonetheless, SORT underestimated mortality. SORT model demonstrated excellent discrimination and calibration predicting perioperative mortality in patients undergoing (1) open surgery, (2) emergency/acute surgery, and (3) in cases with colon-located cancer. CONCLUSION: SORT is an easily adopted risk-assessment tool, associated with enhanced accuracy, that could be implemented in the perioperative pathway of patients undergoing surgery for colorectal cancer.
BACKGROUND: The accurate evaluation of perioperative risk is crucial to facilitate the shared decision-making process. Surgical outcome risk tool (SORT) has been developed to provide enhanced and more feasible identification of high-risk surgical patients. Nonetheless, SORT has not been validated for patients with colorectal cancer undergoing surgery. Our aim was to determine whether SORT can accurately predict mortality after surgery for colorectal cancer and to compare it with traditional risk models. METHOD: 526 patients undergoing surgery performed by a colorectal surgical team in a single Greek tertiary hospital (2011-2019) were included. Five risk models were evaluated: (1) SORT, (2) Physiology and Operative Severity Score for the enumeration of Mortality and Morbidity (POSSUM), (3) Portsmouth POSSUM (P-POSSUM), (4) Colorectal POSSUM (CR-POSSUM), and (5) the Association of Great Britain and Ireland (ACPGBI) score. Model accuracy was assessed by observed to expected (O:E) ratios, and area under Receiver Operating Characteristic curve (AUC). RESULTS: Ten patients (1.9%) died within 30 days of surgery. SORT was associated with an excellent level of discrimination [AUC:0.81 (95% CI:0.68-0.94); p = 0.001] and provided the best performing calibration of all models in the entire dataset analysis (H-L:2.82; p = 0.83). Nonetheless, SORT underestimated mortality. SORT model demonstrated excellent discrimination and calibration predicting perioperative mortality in patients undergoing (1) open surgery, (2) emergency/acute surgery, and (3) in cases with colon-located cancer. CONCLUSION: SORT is an easily adopted risk-assessment tool, associated with enhanced accuracy, that could be implemented in the perioperative pathway of patients undergoing surgery for colorectal cancer.
Authors: Cherng H Kong; Glenn D Guest; Douglas A Stupart; Ian G Faragher; Steven T F Chan; David A Watters Journal: Dis Colon Rectum Date: 2013-07 Impact factor: 4.585
Authors: Lindsey A Torre; Freddie Bray; Rebecca L Siegel; Jacques Ferlay; Joannie Lortet-Tieulent; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2015-02-04 Impact factor: 508.702
Authors: Suneetha Ramani Moonesinghe; Michael G Mythen; Priya Das; Kathryn M Rowan; Michael P W Grocott Journal: Anesthesiology Date: 2013-10 Impact factor: 7.892
Authors: Nigel M Bagnall; Edward T Pring; George Malietzis; Thanos Athanasiou; Omar D Faiz; Robin H Kennedy; John T Jenkins Journal: Int J Colorectal Dis Date: 2018-08-04 Impact factor: 2.571