Prediction of 30-day, 90-day and 1 year mortality after colorectal cancer surgery using a data-driven approach

Abstract

Abstract Purpose: To develop prediction models for short-term mortality risk assessment following colorectal cancer surgery. Background: Individual risk prediction for mortality following CRC surgery can assist surgeons in clinical decision-making and patient-centered care. Several risk scoring systems exist to assess the risk of postoperative mortality following a CRC resection, but they are based on intra- or postoperative variables or do not represent contemporary clinical practice. Methods: Data was harmonized from four Danish observational health databases into the Observational Medical Outcomes Partnership Common Data Model. With a data-driven approach using the Least Absolute Shrinkage and Selection Operator logistic regression on preoperative data, we developed 30-day, 90-day, and 1-year mortality prediction models. We assessed discriminative performance using the area under the receiver operating characteristic and precision-recall curve and calibration using calibration slope, intercept, and calibration-in-the-large. We additionally assessed model performance in subgroups of curative, palliative, elective, and emergency surgery. Results: A total of 57,558 patients were included in the study population. The model showed good discrimination with an area under the receiver-operating characteristic curve of 0.88, 0.878, and 0.861 for 30-day, 90-day, and 1-year mortality respectively, and a calibration-in-the-large of 1.01, 0.99, and 0.99. Conclusion: We were able to train prediction models for the risk of short-term mortality on a data set of four combined national health databases with good discrimination and calibration.