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

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.

Fig. 1

Cohort definition Description of the study cohort, including cohort exclusions with reasons. The DCCG.dk cohort included all Danish patients registered in the nationwide Danish Colorectal Cancer Group (DCCG.dk) database with a diagnosis of colorectal cancer (CRC) between 1 January 2004 and 31 December 2015

Fig. 2

Model discrimination characteristics a Receiver operating characteristic curve for the least absolute shrinkage and selection operator regression model test set (n = 8231). Area under the receiver operating characteristic (AUROC) curve = 85.28 (95% c.i. 83.55 to 85.01). The false-positive rate is 1 – specificity. The diagonal line indicates the neutral predictive value (AUROC = 50.0). b Precision-recall plot with an area under the precision-recall curve of 31.40. Recall (sensitivity) and precision (positive predictive value) are shown on the x- and y-axes, respectively. The horizontal line indicates the neutral predictive value (positive predictive value of 5.3, i.e., average population risk of death). c F1 score versus prediction threshold for 90-day mortality after colorectal cancer surgery. The F1 score represents a balanced measure of overall classifier performance, combining both precision and recall with equal weights. The classifier performance of the model was optimal at model threshold of 0.2. d Model prediction score distribution. The prediction score distribution represents the predicted risk distribution for those with and without the outcome. The more these curves overlap, the worse the model discrimination performance

Fig. 3

Model calibration a Calibration plot for the least absolute shrinkage and selection operator regression model test set (n = 8231). Bars indicate 95% confidence intervals. Calibration gradient = 1.04. Intercept = -0.00. b Demographic calibration plots for across gender and age groups of 5-year intervals