ArticleNorth American Spine Society journal2024
PROPOSE. Development and validation of a prediction model for shared decision making for patients with lumbar spinal stenosis.
Article in North American Spine Society journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- Generalisable prediction models for outcomes after lumbar spinal stenosis surgery: a model development and external validation study.EClinicalMedicine · 2026Article
- Article
- Revolutionizing spine surgery with emerging AI-FEA integration.Journal of robotic surgery · 2025Review
- Leveraging artificial intelligence for collaborative care planning: Innovations and impacts in shared decision-making - A systematic review.Open medicine (Warsaw, Poland) · 2025Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Decompression for lumbar spinal stenosis (LSS) is the most frequently performed spine surgery in Denmark. According to the Danish spine registry DaneSpine, at 1 year after surgery, about 75% of patients experiences considerable pain relief and around 66% improvement in quality of life. However, 25% do not improve very much. We have developed a predictive decision support tool, PROPOSE. It is intended to be used in the clinical conversation between healthcare providers and LSS patients as a shared decision-making aid presenting pros and cons of surgical intervention. This study presents the development and evaluation of PROPOSE in a clinical setting. Methods: For model development, 6.357 LSS patients enrolled in DaneSpine were identified. For model validation, predictor response and predicted outcome was collected via PROPOSE from 228 patients. Observed outcome at 1 year was retrieved from DaneSpine. All participants were treated at 3 Danish spine centers. The outcome measures presented are improvement in walking distance, the Oswestry Disability Index, EQ-5D-3L and leg/back pain on the Visual Analog Scale. Outcome variables were dichotomized into success (1) and failure (0). With the exception of walking distance, a success was defined as reaching minimal clinically important difference at 1-year follow-up. Models were trained using Multivariate Adaptive Regression Splines. Performance was assessed by inspecting confusion matrix, ROC curves and comparing GCV (generalized cross-validation) errors. Final performance of the models was evaluated on independent test data. Results: The walking distance model demonstrated excellent performance with an AUC of 0.88 and a Brier score of 0.14. The VAS leg pain model had the lowest discriminatory performance with an AUC of 0.67 and a Brier score of 0.22. Conclusions: PROPOSE works in a real-world clinical setting as a proof of concept and demonstrates acceptable performance. It may have the potential of aiding shared decision making.
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