Nomogram Prediction Model for Deep Venous Thrombosis in Traumatic Cervical Spinal Cord Injury Patients During Hospitalization.

Abstract

To explore the risk factors of deep venous thrombosis (DVT) in patients with traumatic cervical spinal cord injury (SCI) during hospitalization, and to establish and verify a nomogram model. A total of 580 patients with traumatic cervical SCI were enrolled in this study. The general information and laboratory indicators of the patients were collected. Duplex ultrasound was used to diagnose the DVT. The general data of the 2 groups were compared and logistic regression analysis was performed to identify independent risk factors for DVT. Based on these identified risk factors, a nomogram model was developed. The accuracy and clinical utility of the model were assessed using the area under the receiver operating characteristic curve, calibration curve, and decision curve analysis. The independent risk factors for DVT in patients with traumatic cervical SCI were: American Spinal Cord Injury Association grade (P < 0.001) and combined craniocerebral injury (P = 0.020), the neutrophil-to-lymphocyte ratio (P < 0.001), D-dimer (P < 0.001). According to the above 4 independent risk factors, a nomogram model was constructed. The area under the receiver operating characteristic curve of the nomogram was 0.808 (95% confidence interval=0.754-0.862) in the training cohort and 0.785 (95% confidence interval=0.668-0.902) in the validation cohort, respectively. It indicates that this model has a good ability to predict the risk of DVT. The calibration curve and decision curve analysis demonstrated that the model exhibited excellent accuracy and clinical effectiveness. The American Spinal Cord Injury Association grade, combined craniocerebral injury, the neutrophil-to-lymphocyte ratio, and D-dimer, represents independent risk factors for DVT in patients with traumatic cervical SCI during hospitalization. Furthermore, the prediction model developed based on these factors demonstrates robust predictive performance.