Development and Validation of a Predictive Model for Acute Myelitis Secondary to Hyperextension-Induced Spinal Cord Injury in Paediatric Patients

Honghui Lei¹Haoran Yin¹Fang-Yong Wang^1*Yang Yu¹Wenjie Zhang¹Meiling Cheng²Sitong Su¹

• ¹School of Rehabilitation, Capital Medical University, Beijing, China
• ²The Second Clinical College of Wenzhou Medical University, Wenzhou Medical University, Zhejiang, China

Background: The incidence of pediatric acute hyperextension-induced spinal cord injury (PAHSCI) is increasing in China, with some cases complicated by acute transverse myelitis (ATM). As risk of ATM in PAHSCI predictive tools are lacking, this study aims to develop a clinical-imaging nomogram to assess ATM risk and support precision diagnosis and treatment in PAHSCI. Methods: We retrospectively analyzed clinical data from patients under 14 years of age diagnosed with thoracic PAHSCI between January 2012 and January 2023. All patients underwent lumbar puncture, gadolinium-enhanced imaging, and whole-spine MRI. Clinical history and imaging findings were collected, and the diagnosis of ATM was determined according to the Transverse Myelitis Consortium Working Group criteria. Patients were randomly assigned to training and validation cohorts in a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used to identify potential risk factors for ATM, which were then incorporated into a multivariable logistic regression model to construct a predictive nomogram. Model discrimination and calibration were assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and Brier scores. Internal validation was performed via 1,000-bootstrap resampling to generate 95% confidence intervals. Model goodness-of-fit was evaluated with the Hosmer – Lemeshow test, and clinical utility was assessed using decision curve analysis (DCA). Results: LASSO regression and multivariate logistic regression identified five predictors: age, fall, latent activity, flow void, pinprick sensation score, which were used to construct a nomogram for estimating the risk of ATM in PAHSCI patients. The model demonstrated strong discriminative performance, with AUCs of 0.876 (95% CI: 0.803–0.950) in the training set and 0.844 (95% CI: 0.709–0.979) in the validation set. Calibration was satisfactory in both cohorts, as evidenced by the Hosmer–Lemeshow test (training: χ²=5.638, P=0.776; validation: χ²=9.666, P=0.378) and low Brier scores (0.138 and 0.167, respectively). Decision curve analysis indicated substantial net clinical benefit within risk thresholds of 8–99% in the training cohort and 6–71% in the validation cohort. Conclusion: We developed a preliminary nomogram demonstrating strong predictive accuracy for estimating ATM risk in PAHSCI patients, thereby enabling clinicians to adopt individualized therapeutic strategies.