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Prediction Models in Degenerative Spine Surgery: A Systematic Review
Daniel Lubelski1, Andrew Hersh1, Tej D Azad1
11500Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Global Spine Journal
|April 23, 2021
Summary
This systematic review found significant variation in prediction models for degenerative spine surgery outcomes. While many models are internally validated, external validation is needed for online deployment to optimize patient care.
Area of Science:
- Spine Surgery
- Medical Prediction Models
- Health Outcomes Research
Background:
- Prediction models are crucial for forecasting outcomes in degenerative spinal surgery.
- Existing literature shows heterogeneity in model development and validation.
Purpose of the Study:
- To systematically review prediction models for postoperative outcomes in degenerative spine surgery.
- To identify trends in model development, validation, and application.
Main Methods:
- A systematic review of PubMed/Medline and Embase databases was performed.
- Articles published between January 1, 2000, and March 1, 2020, focusing on elective degenerative spine surgery prediction models were included.
Main Results:
- Thirty-one articles were analyzed, with most focusing on thoracolumbar spine surgery.
- Machine learning and logistic regression were the most common development techniques (42% each).
- Web-based calculators were present in 45% of studies, investigating diverse outcomes like complications, pain, and return to work.
Conclusions:
- Significant heterogeneity exists in prediction model development for degenerative spine surgery.
- Most models undergo internal validation, with limited external validation.
- Further external validation is recommended to enable online deployment for optimizing patient and administrative use.
