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September 5, 2025Journal of Musculoskeletal and Neuronal InteractionsOpen Access

LASSO Logistic Regression was Used to Analyze the Risk Factors for Cauda Equina Injury Secondary to Lumbar Spinal Stenosis and to Build a Risk Model

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Authors

KLKai LiuYWYue WuPMPengfei Ma

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Overview

This observational analysis identifies key risk factors for cauda equina injury in patients with lumbar spinal stenosis, suggesting a valuable prediction model.

Key Points

  • The risk nomogram model achieved an area under the curve (AUC) of 0.865, indicating strong predictive ability for cauda equina injury.
  • Sensitivity and specificity of the model were 91.11% and 93.64%, respectively, demonstrating its reliability in clinical application.
  • LASSO logistic regression was employed to analyze risk factors, revealing age, disease duration, and spinal canal dimensions as significant predictors.
  • The risk prediction model was validated with a C-index of 0.823, highlighting its potential for improved patient outcomes.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68bb46bd6d6d5674bccfeb02https://doi.org/10.22540/jmni-25-299
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