Prediction of 30-day, 90-day, and 1-year mortality after colorectal cancer surgery using a data-driven approach
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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.
Originalsprog | Engelsk |
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Artikelnummer | 31 |
Tidsskrift | International Journal of Colorectal Disease |
Vol/bind | 39 |
Udgave nummer | 1 |
Antal sider | 11 |
ISSN | 0179-1958 |
DOI | |
Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:
Open access funding provided by Copenhagen University This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement no 806968. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. Funding was also provided by the Region Zealand and the Danish Ministry of Higher Education and Science. Additionally, the project received financial support from the Novo Nordisk Foundation Project title: Personalized medicine infrastructure using the open-source OMOP common data model including an Electronic Health Record interface grant number: NNF21OC0069821.
Funding Information:
The authors thank the Danish Health Data Authority, the Danish Clinical Quality Program, and the Danish Colorectal Cancer Group for access to data. We thank edenceHealth for assistance in transformation of data to OMOP-CDM format. Additionally, we thank the European Health Data Evidence Network (EHDEN) for sparring during the process of data transformation to OMOP-CDM and the PatientLevelPrediction work group in the OHDSI community for development of open source tools making analysis work for this paper possible.
Publisher Copyright:
© The Author(s) 2024.
ID: 390407676