Establishment and verification of a surgical prognostic model for cervical spinal cord injury without radiological abnormality.

ABSTRACT: Some studies have suggested that early surgical treatment can effectively improve the prognosis of cervical spinal cord injury without radiological abnormality, but no research has focused on the development of a prognostic model of cervical spinal cord injury without radiological abnormality. This retrospective analysis included 43 patients with cervical spinal cord injury without radiological abnormality. Seven potential factors were assessed: age, sex, external force strength causing damage, duration of disease, degree of cervical spinal stenosis, Japanese Orthopaedic Association score, and physiological cervical curvature. A model was established using multiple binary logistic regression analysis. The model was evaluated by concordant profiling and the area under the receiver operating characteristic curve. Bootstrapping was used for internal validation. The prognostic model was as follows: logit(P) = -25.4545 + 21.2576VALUE + 1.2160SCORE - 3.4224TIME, where VALUE refers to the Pavlov ratio indicating the extent of cervical spinal stenosis, SCORE refers to the Japanese Orthopaedic Association score (0-17) after the operation, and TIME refers to the disease duration (from injury to operation). The area under the receiver operating characteristic curve for all patients was 0.8941 (95% confidence interval, 0.7930-0.9952). Three factors assessed in the predictive model were associated with patient outcomes: a great extent of cervical stenosis, a poor preoperative neurological status, and a long disease duration. These three factors could worsen patient outcomes. Moreover, the disease prognosis was considered good when logit(P) ? -2.5105. Overall, the model displayed a certain clinical value. This study was approved by the Biomedical Ethics Committee of the Second Affiliated Hospital of Xi'an Jiaotong University, China (approval number: 2018063) on May 8, 2018.

SUBMITTER: Wang J 

PROVIDER: S-EPMC6352577 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature