Developing prediction models for short-term mortality after surgery for colorectal cancer using a Danish national quality assurance database.

Autor: Bräuner KB; Center for Surgical Science, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark. karob@regionsjaelland.dk., Rosen AW; Center for Surgical Science, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark., Tsouchnika A; Center for Surgical Science, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark., Walbech JS; Center for Surgical Science, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark., Gögenur M; Center for Surgical Science, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark., Lin VA; Center for Surgical Science, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark., Clausen JSR; Center for Surgical Science, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark., Gögenur I; Center for Surgical Science, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark.; The Faculty of Health Science, University of Copenhagen, Blegdamsvej 6, 2200, Copenhagen N, Denmark.
Jazyk: angličtina
Zdroj: International journal of colorectal disease [Int J Colorectal Dis] 2022 Aug; Vol. 37 (8), pp. 1835-1843. Date of Electronic Publication: 2022 Jul 18.
DOI: 10.1007/s00384-022-04207-6
Abstrakt: Purpose: The majority of colorectal cancer surgeries are performed electively, and treatment is often decided at the multidisciplinary team conference. Although the average 30-day mortality rate is low, there is substantial population heterogeneity from young, healthy patients to frail, elderly patients. The individual risk of surgery can vary widely, and tailoring treatment for colorectal cancer may lead to better outcomes. This requires prediction of risk that is accurate and available prior to surgery.
Methods: Data from the Danish Colorectal Cancer Group database was transformed into the Observational Medical Outcomes Partnership Common Data Model. Models were developed to predict the risk of mortality within 30, 90, and 180 days after colorectal cancer surgery using only covariates decided at the multidisciplinary team conference. Several machine-learning models were trained, but due to superior performance, a Least Absolute Shrinkage and Selection Operator logistic regression was used for the final model. Performance was assessed with discrimination (area under the receiver operating characteristic and precision recall curve) and calibration measures (calibration in large, intercept, slope, and Brier score).
Results: The cohort contained 65,612 patients operated for colorectal cancer in the period from 2001 to 2019 in Denmark. The Least Absolute Shrinkage and Selection Operator model showed an area under the receiver operating characteristic for 30-, 90-, and 180-day mortality after colorectal cancer surgery of 0.871 (95% CI: 0.86-0.882), 0.874 (95% CI: 0.864-0.882), and 0.876 (95% CI: 0.867-0.883) and calibration in large of 1.01, 0.98, and 1.01, respectively.
Conclusion: The postoperative short-term mortality prediction model showed excellent discrimination and calibration using only preoperatively known predictors.
(© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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