Related Experiment Video
Updated: Jun 10, 2026
Evaluation of Patients' Posture and Gait Profile After Lumbar Fusion Surgery by Video Rasterstereography and Treadmill Gait Analysis
Published on: March 23, 2019
Generalisable prediction models for outcomes after lumbar spinal stenosis surgery: a model development and external
Bjørnar Berg1, Allan Abbott2,3, Casper Friis Pedersen4
1Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
Background:
Lumbar spinal stenosis is a leading indication for spine surgery, but outcomes are heterogeneous. We aimed to develop and externally validate prediction models for 12-month disability and pain to inform shared decision-making.
Methods:
This registry-based multicentre cohort study used data from three national spine registries of patients (≥16 years) undergoing elective lumbar spinal stenosis surgery. Data from the Norwegian Registry for Spine Surgery (NORspine, 2007-2023) were used for model development and internal-external cross-validation (IECV). External validation was carried out in the Swedish Registry (SweSpine, 2016-2022) and Danish Registry (DaneSpine, 2009-2022) with data collected by the Spine Centre of Southern Denmark. The primary outcome was the Oswestry Disability Index (ODI) at 12 months, modelled as a continuous and binary measure (acceptable symptom state). Secondary outcomes were Numeric Rating Scale (NRS) back and leg pain at 12 months. Logistic regression, linear regression, and XGBoost models were applied with 16 predictors. Missing data were handled using multiple imputation. Performance was assessed by calibration, mean absolute error (MAE), adjusted R2, and C-statistics. This study is registered with Open Science Framework (https://osf.io/qz27b/).
Findings:
The development cohort included 31,908 patients (52.4% female, 47.6% male). The external validation cohorts included 30,700 from SweSpine (52.8% female, 47.2% male) and 4063 from DaneSpine (54.6% female, 45.4% male). Twelve-month outcome completeness was 77% in the development cohort and ranged from 66% to 80% across the external validation cohorts. For ODI, linear regression achieved a pooled MAE of 12.4 (95% CI 11.8-13.1) after IECV, and 13.3 (95% CI 13.2-13.4) and 12.3 (95% CI 12.0-12.7) at external validation. Adjusted R2 values ranged from 0.26 to 0.33. Calibration was acceptable, with slopes near 1 and calibration-in-the-large ranging from -0.47 after IECV to 1.28-1.54 at external validation, indicating minor systematic underprediction. The binary ODI model achieved C-statistics of 0.75 (95% CI 0.74-0.76) after IECV, and 0.78 (95% CI 0.78-0.79) and 0.76 (95% CI 0.74-0.77) at external validation. Pain models showed lower performance (MAE 2.2-2.6; C-statistics 0.64-0.73). XGBoost yielded similar results.
Interpretation:
Models predicting disability and pain were well calibrated and generalisable across Scandinavian countries, with the best overall performance for disability. These findings provide a foundation for prospective evaluation in future studies to determine the impact on decision-making and patient outcomes in clinical practice.
Funding:
Research Council of Norway.
