Development and validation of a novel nomogram for predicting mechanical ventilation after cervical spinal cord injury.

Objective: This study aimed to investigate the risk factors relating to the need for mechanical ventilation (MV)in isolated cervical spinal cord injury (cSCI) patients and to construct a nomogram prediction model.

Design: Retrospective analysis study.

Participants: A total of 5784 patients (N=5784) who had a cSCI were admitted to the National Spinal Cord Injury Model System database (NSCID) between 2006 and 2021.

Setting: NSCID observation data was initially collected during rehabilitation hospitalization.

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 cSCI patients, these independent influencing factors were used to develop a nomogram prediction model. The area under the receivers operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA) were employed 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 of MV. Logistic regression analysis demonstrated that associated injury, American Spinal Cord Injury Association (ASIA) impairment scale (AIS), the sum of unilateral optimal motor scores for each muscle segment of upper extremities (sUEM) and neurological level of injury (NLI) were independent predictors of the use of MV (p<0.05). The prediction nomogram of MV usage in cSCI patients 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 a good calibration and clinical practicability.

Conclusion: The nomograph prediction model based on sUEM, NLI, associated injury and AIS can accurately and effectively predict the risk of MV in cSCI patients, to help clinicians screen high-risk patients and formulate targeted intervention measures.