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Developing clinical algorithm for identifying acute lumbar spondylolysis in elementary school children -

Masashi Aoyagi1,2, Kei Naito1, Yuichi Sato1

  • 1Forest Orthopaedic Sports Clinic, Maebashi, Japan.

The Journal of Manual & Manipulative Therapy
|March 28, 2022
PubMed
Summary

This study developed a clinical algorithm to differentiate acute lumbar spondylolysis from nonspecific low back pain in children. Key predictors include lumbar lordosis angle and symptom onset duration for accurate diagnosis.

Keywords:
Lumbar spineclinical decision-makinglow back painstress fracture

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Area of Science:

  • Orthopedics
  • Pediatric Sports Medicine
  • Radiology

Background:

  • Low back pain is common in elementary school-aged children.
  • Differentiating acute lumbar spondylolysis from nonspecific low back pain is clinically challenging.
  • Accurate classification is crucial for appropriate management and imaging decisions.

Purpose of the Study:

  • To develop a clinical algorithm for classifying acute lumbar spondylolysis in elementary school-aged patients.
  • To distinguish spondylolysis from nonspecific low back pain using classification and regression tree analysis.
  • To identify children requiring advanced imaging.

Main Methods:

  • Retrospective review of 73 school-aged patients with acute lumbar spondylolysis or nonspecific low back pain.
  • Development of an algorithm using classification and regression tree analysis on 58 patients.
  • Validation of the algorithm's performance on 15 patients.

Main Results:

  • Lumbar lordosis angle, days after symptom onset, body mass index, and lumbosacral joint angle were identified as key predictors.
  • These factors effectively classified injuries in the developed algorithm.
  • The algorithm demonstrated utility in distinguishing between conditions.

Conclusions:

  • An algorithm was developed to aid in classifying acute lumbar spondylolysis in elementary school-aged children.
  • The algorithm can help identify pediatric patients with low back pain who may need advanced imaging.
  • Further validation with a larger sample size is recommended.