Establishment of a Nomogram model for individualized prediction of the risk of acute spinal cord injury complicated with respiratory dysfunction.
10.12200/j.issn.1003-0034.20231109
- Author:
Jie LIU
1
;
Su-Juan LIU
1
;
Ran LI
1
;
Wen-Jing ZHANG
1
;
Yong WANG
1
Author Information
1. Fuxing Hospital Affiliated to Capital Medical University, Beijing 100038, China.
- Publication Type:Journal Article
- Keywords:
Aacute spinal cord injury;
Column diagram;
Respiratory dysfunction;
Risk factors
- MeSH:
Humans;
Spinal Cord Injuries/complications*;
Nomograms;
Male;
Female;
Middle Aged;
Adult;
Retrospective Studies;
Risk Factors;
Aged;
Respiration Disorders/etiology*;
Adolescent;
Logistic Models
- From:
China Journal of Orthopaedics and Traumatology
2025;38(5):525-531
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To analyze the risk factors of acute spinal cord injury complicated with respiratory dysfunction, and to construct the clinical prediction model of acute spinal cord injury complicated with respiratory dysfunction.
METHODS:Continuous 170 cases of acute spinal cord injury treated from April 2019 to October 2022 were retrospectively collected, and clinical data were uniformly collected. Patients were divided into respiratory dysfunction group 30 cases and non-respiratory dysfunction group 140 cases according to whether they had respiratory dysfunction during treatment. The predictive factors of acute spinal cord injury complicated with respiratory dysfunction were screened by Lasso analysis, and the risk factors of acute spinal cord injury complicated with respiratory dysfunction were screened by multivariate Logistic regression analysis. R(R4.2.1) software was used to establish a nomogram risk warning model for predicting acute spinal cord injury complicated with respiratory dysfunction, and Hosmer-Lemeshow test was used to evaluate the model fit. Finally, area under receiver operating characteristic(ROC) curve (AUC), calibration curve, and decision curve analysis(DCA) were used to evaluate the differentiation, calibration and clinical impact of the model.
RESULTS:The incidence of respiratory dysfunction in 170 patients was 17.65%. Lasso regression analysis selected age, residence, marital status, smoking, hypertension, degree of paralysis, spinal cord injury plane, multiple injuries, spinal cord fracture and dislocation, and ASIA grade as the influencing factors. Multivariate Logistic regression analysis showed that age, smoking, degree of paralysis, level of spinal cord injury, spinal cord injury of fracture and dislocation, and ASIA grade were risk factors for acute spinal cord injury complicated with respiratory dysfunction. The prediction model of acute spinal cord injury complicated with respiratory dysfunction was established by Hosmer-Lemeshow test, χ2=5.830, P=0.67. The AUC value of the model was 0.912. DCA analysis showed that the net benefit value of nomogram prediction of acute spinal cord injury complicated with respiratory dysfunction was higher when threshold probability ranged from 1% to 100%.
CONCLUSION:This column chart can help identify the risk of acute spinal cord injury complicated with respiratory dysfunction in early clinical stage, facilitate early clinical decision-making and intervention, and has important guiding significance for optimizing clinical efficacy and improving prognosis of patients. It is expected to improve and verify this model with larger samples and multi-center in the future.
