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Multivariable Prediction Models for Traumatic Spinal Cord Injury: A Systematic Review
Ramtin Hakimjavadi1, Shahin Basiratzadeh1, Eugene K Wai1,2
1University of Ottawa, Ottawa, Ontario, Canada.
Topics in Spinal Cord Injury Rehabilitation
|March 4, 2024
Summary
Clinical prediction models (CPMs) for traumatic spinal cord injuries (TSCI) need improvement. This review found poor data analysis, inconsistent reporting, and a lack of model presentation formats in existing TSCI CPM research.
Area of Science:
- Spinal cord injury research
- Clinical prediction modeling
- Prognostic research
Background:
- Traumatic spinal cord injuries (TSCI) significantly impact patients and families.
- Accurate prognostication aids treatment, resource allocation, and patient counseling.
- Multivariable clinical prediction models (CPMs) estimate outcome probabilities.
Approach:
- Systematic literature review of CPMs for TSCI.
- Searched major databases (MEDLINE, PubMed, Embase, Scopus, IEEE).
- Focused on predictor selection methods and methodological quality.
Key Points:
- 21 studies (33 CPMs) published 2009-2020 were included.
- High risk of bias in data analysis was common.
- Transparent reporting and model presentation were inconsistent.
- Stepwise selection was the most frequent predictor selection method.
Conclusions:
- TSCI CPMs are valuable for clinicians, researchers, and patients.
- Methodological rigor, transparent reporting, and standardized presentation are crucial for improvement.
- Future research should address identified limitations in CPM development and reporting.
