A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data

BMC Musculoskeletal Disorders, 2023 · DOI: https://doi.org/10.1186/s12891-023-06570-z · Published: July 5, 2023

Spinal Cord Injury Neurology Medical Imaging

Simple Explanation

This study aimed to identify factors that predict irreversible neurological dysfunction (IND) after cervical spinal cord injury (CSCI) and create a tool to estimate the likelihood of IND development. The researchers analyzed data from CSCI patients, identifying key predictors and developing a nomogram, which is a visual representation of a predictive model, and an online calculator for easy use. The model uses clinical and MRI features to assess the probability of IND in CSCI patients, potentially guiding personalized support and treatment strategies.

Study Duration

January 2014 and March 2021

Participants

193 individuals with CSCI

Evidence Level

Not specified

Key Findings

1
Six features, including age, AIS grade, signal of spinal cord (SC), maximum canal compromise (MCC), intramedullary lesion length (IMLL), and specialized institution-based rehabilitation (SIBR), were included in the model.
2
The model demonstrated good prediction accuracy, with a C-index of 0.882 in the training set and 0.827 in external validation.
3
The model exhibited satisfactory consistency and clinical applicability, as verified by calibration curve and decision curve analysis (DCA).

Research Summary

This study constructed a prediction model based on clinical and MRI features to assess the probability of developing irreversible neurological dysfunction (IND) in patients with cervical spinal cord injury (CSCI). The model incorporates six independent predictors: age, American Spinal Injury Association (AIS) grade, signal of the spinal cord, maximum canal compromise, intramedullary lesion length, and specialized institution-based rehabilitation. The developed nomogram and online calculator can assist clinicians in early identification of patients at risk of IND, enabling personalized support and treatment strategies.

Practical Implications

Personalized Treatment Strategies

Early identification of IND risk allows for tailored interventions.

Improved Clinical Decision-Making

The nomogram offers a practical tool for assessing patient prognosis.

Enhanced Patient Support

Understanding IND probability can guide supportive care and rehabilitation planning.

Study Limitations

1
Retrospective study design with potential information bias.
2
Small sample size, which may affect the generalizability of the findings.
3
Exclusion of patients without surgical records, limiting applicability to non-surgical cases.

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