Halicka, Monika, Wilby, Martin, Duarte, Rui and Brown, Christopher 
ORCID: 0000-0003-1414-2635
(2023)
Predicting patient-reported outcomes following lumbar spine surgery: development and external validation of multivariable prediction models.
BMC MUSCULOSKELETAL DISORDERS, 24 (1).
333-.
Abstract
<h4>Background</h4>This study aimed to develop and externally validate prediction models of spinal surgery outcomes based on a retrospective review of a prospective clinical database, uniquely comparing multivariate regression and random forest (machine learning) approaches, and identifying the most important predictors.<h4>Methods</h4>Outcomes were change in back and leg pain intensity and Core Outcome Measures Index (COMI) from baseline to the last available postoperative follow-up (3-24 months), defined as minimal clinically important change (MCID) and continuous change score. Eligible patients underwent lumbar spine surgery for degenerative pathology between 2011 and 2021. Data were split by surgery date into development (N = 2691) and validation (N = 1616) sets for temporal external validation. Multivariate logistic and linear regression, and random forest classification and regression models, were fit to the development data and validated on the external data.<h4>Results</h4>All models demonstrated good calibration in the validation data. Discrimination ability (area under the curve) for MCID ranged from 0.63 (COMI) to 0.72 (back pain) in regression, and from 0.62 (COMI) to 0.68 (back pain) in random forests. The explained variation in continuous change scores spanned 16%-28% in linear, and 15%-25% in random forests regression. The most important predictors included age, baseline scores on the respective outcome measures, type of degenerative pathology, previous spinal surgeries, smoking status, morbidity, and duration of hospital stay.<h4>Conclusions</h4>The developed models appear robust and generalisable across different outcomes and modelling approaches but produced only borderline acceptable discrimination ability, suggesting the need to assess further prognostic factors. External validation showed no advantage of the random forest approach.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Prediction model, Patient-reported outcomes, Spinal surgery, Lumbar spine, Regression, Machine learning, Random forest, Validation, Registry |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 28 Apr 2023 09:57 |
| Last Modified: | 17 May 2023 11:36 |
| DOI: | 10.1186/s12891-023-06446-2 |
| Open Access URL: | https://bmcmusculoskeletdisord.biomedcentral.com/a... |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3170042 |
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