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PROPOSE. Development and validation of a prediction model for shared decision making for patients with lumbar spinal stenosis. | LitMetric

AI Article Synopsis

  • Decompression surgery for lumbar spinal stenosis (LSS) is the most common spine surgery in Denmark, with around 75% of patients experiencing pain relief after one year, but 25% seeing little improvement.
  • A predictive decision support tool called PROPOSE was developed to aid discussions between doctors and LSS patients by presenting the pros and cons of surgery.
  • Evaluation of PROPOSE showed good performance in predicting outcomes for walking distance and leg pain, confirming its effectiveness in real-world clinical settings.

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10831309PMC
http://dx.doi.org/10.1016/j.xnsj.2024.100309DOI Listing

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