# Development and validation of a predictive model for acute myelitis secondary to hyperextension-induced spinal cord injury in pediatric patients

## TL;DR

This study creates a predictive model to assess the risk of acute transverse myelitis in children with spinal cord injuries from hyperextension, aiding in personalized treatment.

## Contribution

A novel clinical-imaging nomogram is developed to predict ATM risk in pediatric PAHSCI patients, enabling precision diagnosis and treatment.

## Key findings

- A nomogram with five predictors (age, fall, latent activity, flow void, and pinprick sensation score) was developed for ATM risk estimation.
- The model showed strong discriminative performance with AUCs of 0.876 in training and 0.844 in validation cohorts.
- Calibration was satisfactory, with low Brier scores and significant net clinical benefit indicated by decision curve analysis.

## Abstract

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 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.

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).

LASSO regression and multivariate logistic regression identified five predictors: age, fall, latent activity, flow void, and 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.

We developed a preliminary nomogram demonstrating strong predictive accuracy for estimating ATM risk in PAHSCI patients, thereby enabling clinicians to adopt individualized therapeutic strategies.