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Updated: Jun 14, 2025
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Nomogram predicting the cardiovascular disease mortality for older patients with colorectal cancer: A real-world
1Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250000, Shandong Province, China.
Background:
Cardio-oncology has received increasing attention especially among older patients with colorectal cancer (CRC). Cardiovascular disease (CVD)-specific mortality is the second-most frequent cause of death. The risk factors for CVD-specific mortality among older patients with CRC are still poorly understood.
Aim:
To identify the prognostic factors and construct a nomogram-based model to predict the CVD-specific mortality among older patients with CRC.
Methods:
The data on older patients diagnosed with CRC were retrieved from The Surveillance, Epidemiology, and End Results database from 2004 to 2015. The prognostic factors and a nomogram-based model predicting the CVD-specific mortality were assessed using least absolute shrinkage and selection operator and Cox regression.
Results:
A total of 141251 eligible patients with CRC were enrolled, of which 41459 patients died of CRC and 12651 patients died of CVD. The age at diagnosis, sex, marital status, year of diagnosis, surgery, and chemotherapy were independent prognostic factors associated with CVD-specific mortality among older patients with CRC. We used these variables to develop a model to predict CVD-specific mortality. The calibration curves for CVD-specific mortality probabilities showed that the model was in good agreement with actual observations. The C-index value of the model in the training cohort and testing cohort for predicting CVD-specific mortality was 0.728 and 0.734, respectively.
Conclusion:
The proposed nomogram-based model for CVD-specific mortality can be used for accurate prognostic prediction among older patients with CRC. This model is a potentially useful tool for clinicians to identify high-risk patients and develop personalized treatment plans.
