Development and validation of a nomogram model for predicting the risk of failed manual reduction in distal radius fractures

Purpose To develop a nomogram model for predicting the risk of failed manual reduction in patients with distal radius fractures (DRF) and to validate its predictive performance. Methods A total of 822 patients with DRF were retrospectively reviewed and divided into two groups based on the success or failure of manual reduction. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. Variables with P < 0.05 in multivariate analysis were used to construct a nomogram using the “rms” package in R software. Internal validation was conducted using bootstrap resampling with 1,000 iterations. Results Eight independent risk factors were incorporated into the predictive model. The receiver operating characteristic curve showed an area under the curve (AUC) of 0.878 (95% CI: 0.855–0.901), with a sensitivity of 75.4%, a specificity of 82.5%, and a Youden's index of 0.579. The calibration curve demonstrated good agreement between predicted and observed probabilities, with a mean absolute error of 0.005 after bootstrap internal validation (1,000 iterations). Conclusion The independent risk factors for failure of manual reduction were determined to be AO classification, severity of swelling, etiology of the fracture, time from injury to reduction, pre-reduction palmar tilt angle, radial shortening height, pre-reduction ulnar deviation angle, and a history of alcohol consumption. The nomogram model constructed based on these factors demonstrates high predictive accuracy for the risk of failed manual reduction in DRF. It has the potential to assist clinicians in more accurately assessing individual patient risk, thereby providing valuable guidance for personalized treatment strategies and clinical decision-making.