Thea Helene Degett1,2, Jane Christensen3, Susanne Oksbjerg Dalton4,5, Kristine Bossen6, Kirsten Frederiksen3, Lene Hjerrild Iversen7,8, Ismail Gögenur9,8. 1. Center for Surgical Science (CSS), Department of Surgery, Zealand University Hospital, Lykkebækvej 1, 4600, Koge, Denmark. theadegett@gmail.com. 2. Survivorship and Inequality in Cancer, Danish Cancer Society Research Center, Copenhagen, Denmark. theadegett@gmail.com. 3. Statistics and Data analysis, Danish Cancer Society Research Center, Copenhagen, Denmark. 4. Survivorship and Inequality in Cancer, Danish Cancer Society Research Center, Copenhagen, Denmark. 5. Department of Clinical Oncology & Palliative Care, Zealand University Hospital, Naestved, Denmark. 6. Danish Regions, Copenhagen, Denmark. 7. Department of Surgery, Aarhus University Hospital, Aarhus, Denmark. 8. Danish Colorectal Cancer Group, Copenhagen, Denmark. 9. Center for Surgical Science (CSS), Department of Surgery, Zealand University Hospital, Lykkebækvej 1, 4600, Koge, Denmark.
Abstract
PURPOSE: The aim of this study was to develop and validate a model to predict 90-day mortality after acute colorectal cancer surgery. METHODS: The model was developed in all patients undergoing acute colorectal cancer surgery in 2014-2016 and validated in a patient group operated in 2017 in Denmark. The outcome was 90-day mortality. Tested predictor variables were age, sex, performance status, BMI, smoking, alcohol, education level, cohabitation status, tumour localization and primary surgical procedure. Variables were selected according to the smallest Akaike information criterion. The model was shrunken by bootstrapping. Discrimination was evaluated with a receiver operated characteristic curve, calibration with a calibration slope and the accuracy with a Brier score. RESULTS: A total of 1450 patients were included for development of the model and 451 patients for validation. The 90-day mortality rate was 19% and 20%, respectively. Age, performance status, alcohol, smoking and primary surgical procedure were the final variables included in the model. Discrimination (AUC = 0.79), calibration (slope = 1.04, intercept = 0.04) and accuracy (brier score = 0.13) were good in the developed model. In the temporal validation, discrimination (AUC = 0.80) and accuracy (brier score = 0.13) were good, and calibration was acceptable (slope = 1.19, intercept = 0.52). CONCLUSION: We developed prediction model for 90-day mortality after acute colorectal cancer surgery that may be a promising tool for surgeons to identify patients at risk of postoperative mortality.
PURPOSE: The aim of this study was to develop and validate a model to predict 90-day mortality after acute colorectal cancer surgery. METHODS: The model was developed in all patients undergoing acute colorectal cancer surgery in 2014-2016 and validated in a patient group operated in 2017 in Denmark. The outcome was 90-day mortality. Tested predictor variables were age, sex, performance status, BMI, smoking, alcohol, education level, cohabitation status, tumour localization and primary surgical procedure. Variables were selected according to the smallest Akaike information criterion. The model was shrunken by bootstrapping. Discrimination was evaluated with a receiver operated characteristic curve, calibration with a calibration slope and the accuracy with a Brier score. RESULTS: A total of 1450 patients were included for development of the model and 451 patients for validation. The 90-day mortality rate was 19% and 20%, respectively. Age, performance status, alcohol, smoking and primary surgical procedure were the final variables included in the model. Discrimination (AUC = 0.79), calibration (slope = 1.04, intercept = 0.04) and accuracy (brier score = 0.13) were good in the developed model. In the temporal validation, discrimination (AUC = 0.80) and accuracy (brier score = 0.13) were good, and calibration was acceptable (slope = 1.19, intercept = 0.52). CONCLUSION: We developed prediction model for 90-day mortality after acute colorectal cancer surgery that may be a promising tool for surgeons to identify patients at risk of postoperative mortality.
Authors: M Vester-Andersen; T Waldau; J Wetterslev; M H Møller; J Rosenberg; L N Jørgensen; J C Jakobsen; A M Møller Journal: Br J Surg Date: 2015-03-18 Impact factor: 6.939
Authors: Morten Vester-Andersen; Tina Waldau; Jørn Wetterslev; Morten Hylander Møller; Jacob Rosenberg; Lars Nannestad Jørgensen; Inger Gillesberg; Henrik Loft Jakobsen; Egon Godthåb Hansen; Lone Musaeus Poulsen; Jan Skovdal; Ellen Kristine Søgaard; Morten Bestle; Jesper Vilandt; Iben Rosenberg; Rasmus Ehrenfried Berthelsen; Jens Pedersen; Mogens Rørbæk Madsen; Thomas Feurstein; Malene Just Busse; Johnny D H Andersen; Christian Maschmann; Morten Rasmussen; Christian Jessen; Lasse Bugge; Helle Ørding; Ann Merete Møller Journal: Trials Date: 2013-02-02 Impact factor: 2.279
Authors: S McPhail; L Elliss-Brookes; J Shelton; A Ives; M Greenslade; S Vernon; E J A Morris; M Richards Journal: Br J Cancer Date: 2013-09-17 Impact factor: 7.640