AUTHOR=Li Chong , Shi Qin , Gong Ya-qin , Zhang Ting , Lu Ke TITLE=Development and validation of a prognostic nomogram for 3-year all-cause mortality risk among elderly patients undergoing surgery for osteoporotic fractures JOURNAL=Frontiers in Medicine VOLUME=11 YEAR=2024 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1284207 DOI=10.3389/fmed.2024.1284207 ISSN=2296-858X ABSTRACT=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.