TY - JOUR

T1 - Prediction of 30-day, 90-day, and 1-year mortality after colorectal cancer surgery using a data-driven approach

AU - Bräuner, Karoline Bendix

AU - Tsouchnika, Andi

AU - Mashkoor, Maliha

AU - Williams, Ross

AU - Rosen, Andreas Weinberger

AU - Hartwig, Morten Frederik Schlaikjær

AU - Bulut, Mustafa

AU - Dohrn, Niclas

AU - Rijnbeek, Peter

AU - Gögenur, Ismail

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024

Y1 - 2024

N2 - Purpose: To develop prediction models for short-term mortality risk assessment following colorectal cancer surgery. 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 57,521 patients were included in the study population, 51.1% male and with a median age of 72 years. 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. The overall incidence of mortality were 4.48% for 30-day mortality, 6.64% for 90-day mortality, and 12.8% for 1-year mortality, respectively. Subgroup analysis showed no improvement of discrimination or calibration when separating the cohort into cohorts of elective surgery, emergency surgery, curative surgery, and palliative surgery. 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. We found that one cohort including all operated patients resulted in better performing models than cohorts based on several subgroups.

AB - Purpose: To develop prediction models for short-term mortality risk assessment following colorectal cancer surgery. 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 57,521 patients were included in the study population, 51.1% male and with a median age of 72 years. 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. The overall incidence of mortality were 4.48% for 30-day mortality, 6.64% for 90-day mortality, and 12.8% for 1-year mortality, respectively. Subgroup analysis showed no improvement of discrimination or calibration when separating the cohort into cohorts of elective surgery, emergency surgery, curative surgery, and palliative surgery. 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. We found that one cohort including all operated patients resulted in better performing models than cohorts based on several subgroups.

KW - Colorectal cancer

KW - Machine learning

KW - Mortality

KW - Postoperative

KW - Prediction model

U2 - 10.1007/s00384-024-04607-w

DO - 10.1007/s00384-024-04607-w

M3 - Journal article

C2 - 38421482

AN - SCOPUS:85186467170

VL - 39

JO - International Journal of Colorectal Disease

JF - International Journal of Colorectal Disease

SN - 0179-1958

IS - 1

M1 - 31

ER -