Prediction of 90-day mortality after surgery for colorectal cancer using standardized nationwide quality-assurance data

Author:

Vogelsang R P1ORCID,Bojesen R D12ORCID,Hoelmich E R1,Orhan A1,Buzquurz F1ORCID,Cai L1,Grube C1,Zahid J A1,Allakhverdiiev E13,Raskov H H1,Drakos I1,Derian N1,Ryan P B45,Rijnbeek P R6,Gögenur I17

Affiliation:

1. Center for Surgical Science, Department of Surgery, Zealand University Hospital, Koege, Denmark

2. Department of Surgery, Slagelse Hospital, Slagelse, Denmark

3. Odysseus Data Services Inc., Cambridge, Massachusetts, USA

4. Department of Medical Informatics, Janssen Research & Development LLC, Raritan, New Jersey, USA

5. Columbia University, New York, New York, USA

6. Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, The Netherlands

7. Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

Abstract

Abstract Background Personalized risk assessment provides opportunities for tailoring treatment, optimizing healthcare resources and improving outcome. The aim of this study was to develop a 90-day mortality-risk prediction model for identification of high- and low-risk patients undergoing surgery for colorectal cancer. Methods This was a nationwide cohort study using records from the Danish Colorectal Cancer Group database that included all patients undergoing surgery for colorectal cancer between 1 January 2004 and 31 December 2015. A least absolute shrinkage and selection operator logistic regression prediction model was developed using 121 pre- and intraoperative variables and internally validated in a hold-out test data set. The accuracy of the model was assessed in terms of discrimination and calibration. Results In total, 49 607 patients were registered in the database. After exclusion of 16 680 individuals, 32 927 patients were included in the analysis. Overall, 1754 (5.3 per cent) deaths were recorded. Targeting high-risk individuals, the model identified 5.5 per cent of all patients facing a risk of 90-day mortality exceeding 35 per cent, corresponding to a 6.7 times greater risk than the average population. Targeting low-risk individuals, the model identified 20.9 per cent of patients facing a risk less than 0.3 per cent, corresponding to a 17.7 times lower risk compared with the average population. The model exhibited discriminatory power with an area under the receiver operating characteristics curve of 85.3 per cent (95 per cent c.i. 83.6 to 87.0) and excellent calibration with a Brier score of 0.04 and 32 per cent average precision. Conclusion Pre- and intraoperative data, as captured in national health registries, can be used to predict 90-day mortality accurately after colorectal cancer surgery.

Funder

Innovative Medicines Initiative 2 Joint undertaking

European Union's Horizon 2020 research and innovation program and EFPIA

Publisher

Oxford University Press (OUP)

Subject

General Medicine

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