A predictiament injuryve nomogram for cervical anterior longitudinal ligament injury.

Abstract

To develop a nomogram-based predictive model for anterior longitudinal ligament (ALL) injury caused by cervical spine trauma. A total of 256 patients with cervical hyperextension injuries were included in this study. Univariate and multivariable logistic regression analyses were used to select the predictive variables, and subsequently, a nomogram model was developed. Finally, the model was validated using both the training and validation datasets. The nomogram model included five predictive factors: thickness of prevertebral soft tissue (TOPST), intervertebral disk angle (IDA), avulsion fracture of the anterior edge of the vertebral body (AFOA), ALL disruption observed in T1-weighted sequence (T1D), and high signal intensity in T2-weighted sequence (T2HS). The areas under the curve (AUC) for the training and validation sets were 0.986 and 0.987, respectively. The calibration curves for both the training and validation sets showed slopes close to 1, indicating good calibration. Decision curve analysis demonstrated that the model performed well and was feasible for making beneficial clinical decisions. The nomogram model based on TOPST, IDA, AFOA, T1D, and T2HS is a reliable tool for predicting cervical ALL injury.