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Updated: May 10, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a prognostic nomogram for 3-year all-cause mortality risk among elderly patients
Chong Li1, Qin Shi2, Ya-Qin Gong3
1Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, Suzhou, Jiangsu, China.
Introduction:
To develop and validate a comprehensive prognostic model for the mid-to-long term mortality risk among ≥50-year-old osteoporotic fracture (OPF) surgical patients.
Methods:
Our retrospective investigation included data from the Osteoporotic Fracture Registration System established by the Affiliated Kunshan Hospital of Jiangsu University, and involved 1,656 patients in the development set and 675 patients in the validation set. Subsequently, we employed a multivariable Cox regression model to establish a 3-year mortality predicting nomogram, and the model performance was further evaluated using C-index and calibration plots. Decision curve analysis (DCA) was employed to assess feasibility of the clinical application of this model.
Results:
Using six prognostic indexes, namely, patient age, gender, the American Society of Anesthesiologists (ASA) score, the Charlson comorbidity index (CCI), fracture site, and fracture liaison service (FLS), we generated a simple nomogram. The nomogram demonstrated satisfactory discrimination within the development (C-index = 0.8416) and validation (C-index = 0.8084) sets. Using calibration plots, we also revealed good calibration. The model successfully classified patients into different risk categories and the results were comparable in both the development and validation sets. Finally, a 1-70% probability threshold, according to DCA, suggested that the model has promise in clinical settings.
Conclusion:
Herein, we offer a robust tool to estimating the 3-year all-cause mortality risk among elderly OPF surgical patients. However, we recommend further assessments of the proposed model prior to widespread clinical implementation.

