Prognosis and conditional nomogram of cervical spine fracture in patients with severe spinal cord injury: a multicenter retrospective study

Abstract

Introduction: Cervical spine fractures with severe spinal cord injury (SCI) are common following cervical spine trauma and are associated with a high mortality rate. Understanding the mortality patterns of patients with cervical spine fractures and severe SCI can offer valuable evidence to surgeons and family members who are required to make critical healthcare decisions. The authors aimed to evaluate the instantaneous death risk and conditional survival (CS) of such patients and developed conditional nomograms to account for different periods of survivors and predict the survival rates.

Methods: Their instantaneous death risks were calculated using the hazard function, and the Kaplan-Meier method was used to evaluate the survival rates. Cox regression was used to choose the variables for the construction of the nomograms. The area under the receiver operating characteristic curve and calibration plots were used to validate the performance of the nomograms.

Results: The authors finally included 450 patients with cervical spine fractures and severe SCI using propensity score matching. The instantaneous death risk was the highest during the first 12 months after injury. Surgical treatment can help decrease the instantaneous death risk quickly, especially in early-term surgery. The 5-year CS increased constantly from 73.3% at baseline to 88.0% after 2 years of survival. Conditional nomograms were constructed at baseline and in those who survived for 6 and 12 months. The area under the receiver operating characteristic curve and calibration curves indicated that the nomograms had a good performance.

Conclusion: Their results improve our understanding of the instantaneous death risk of patients in different periods following injury. CS demonstrated the exact survival rate among medium-term and long-term survivors. Conditional nomograms are suitable for different survival periods in predicting the probability of survival. Conditional nomograms help in understanding the prognosis and improve the shared decision-making approaches.