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Updated: Jun 11, 2025
A Murine Model of Cervical Spinal Cord Injury to Study Post-lesional Respiratory Neuroplasticity
Published on: May 28, 2014
Development and Validation of a Novel Nomogram for Predicting Mechanical Ventilation After Cervical Spinal Cord
Guozhen Liu1, Lei Liu2, Ze Zhang3
1Department of Spinal Surgery, General Hospital of Ningxia Medical University, Yinchuan, China; Southeast University, Nanjing, Jiang Su Province, China.
Objective:
To investigate the risk factors relating to the need for mechanical ventilation (MV) in isolated patients with cervical spinal cord injury (cSCI) and to construct a nomogram prediction model.
Design:
Retrospective analysis study.
Setting:
National Spinal Cord Injury Model System Database (NSCID) observation data were initially collected during rehabilitation hospitalization.
Participants:
A total of 5784 patients (N=5784) who had a cSCI were admitted to the NSCID between 2006 and 2021.
Interventions:
Not applicable.
Main Outcome Measure(S):
A univariate and multivariate logistic regression analysis was used to identify the independent factors affecting the use of MV in patients with cSCI, and these independent influencing factors were used to develop a nomogram prediction model. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency and the clinical application value of the model, respectively.
Results:
In a series of 5784 included patients, 926 cases (16.0%) were admitted to spinal cord model system inpatient rehabilitation with the need for MV. Logistic regression analysis demonstrated that associated injury, American Spinal Cord Injury Association Impairment Scale (AIS), the sum of unilateral optimal motor scores for each muscle segment of upper extremities (sUEM), and neurologic level of injury (NLI) were independent predictors for the use of MV (P<.05). The prediction nomogram of MV usage in patients with cSCI was established based on the above independent predictors. The AUROC of the training set, internal verification set, and external verification set were 0.871 (0.857-0.886), 0.867 (0.843-0.891), and 0.850 (0.824-0.875), respectively. The calibration curve and DCA results showed that the model had good calibration and clinical practicability.
Conclusions:
The nomograph prediction model based on sUEM, NLI, associated injury, and AIS can accurately and effectively predict the risk of MV in patients with cSCI, to help clinicians screen high-risk patients and formulate targeted intervention measures.
