Predicting patient-reported outcomes following lumbar spine surgery: development and external validation of multivariable prediction models.

ABSTRACT:

Background

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.

Methods

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 v

SUBMITTER: Halicka M 

PROVIDER: S-EPMC10134672 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature